International Journal of Electronic Security and Digital Forensics (70 papers in press)

Regular Issues

• The Legal Authority of the Electronic Authentication Certificate and its Role in Proving E-Commerce Transactions
  by Lana Al-Khalaileh, Tareq Al-billeh, Ali Al-Hammouri 
  Abstract: This article analyses the concept of electronic authentication certificate and shows its types issued by electronic authentication authorities according to the function they perform and the purpose of their issuance. It will also show the legal recognition of electronic certificates by referring to the national legislations in Jordan, Egypt, France and Tunisia, and the extent to which these legislations comply with international requirements. In this study, light will also be shed on achieving trust and safety among dealers through modern means of communication, especially via the Internet, and on encouraging dealing with electronically signed documents through the use of a reliable, neutral third party. This party will be responsible for verifying the integrity of the electronic certificates and the validity of their issuance, as well as ensuring the seriousness of the dealing and that it is free from fraud.
  Keywords: electronic documentation; legal authority; electronic documents; international requirements; electronic commerce; electronic transactions.
  
  
• Feature-driven Anomalous Behaviour Detection and Incident Classification Model for ICS in Water Treatment Plants
  by Gabriela Ahmadi-Assalemi, Haider Al-Khateeb, Tanaka Laura Makonese, Vladlena Benson, Samiya Khan, Usman Butt 
  Abstract: Industry 5.0 envisions humans working alongside emerging technologies and enabled by the fusion of devices and sensors using information and communication technologies (ICT) to facilitate process automation, monitoring and distributed control in industrial control systems (ICS). However, the application of disruptor technologies and exposure of insecure devices broadens the attack surface making ICS an attractive target for sophisticated threat actors. Furthermore, ICS deliver a range of critical services hence disruption of industrial operations and services could have serious consequences. This study proposes an anomaly-based intrusion detection system for a water treatment plant based on a new model to determine variable significance for improved detection accuracy using machine learning (ML) algorithms coupled with incident classification based on functional impact. Determining statistical significance for independent ICS variables was addressed using logistic regression. Overall, 39 variables are deemed relevant in diagnosing the system state of the ICS operation to be expected or under attack. Our approach is validated using the secure water treatment (SWaT) testbed. Experimental results reveal that anomaly detection was effective using k-NN, ANN and SVM achieving an F1-score of 0.99, 0.98 and 0.97 respectively.
  Keywords: critical national infrastructure; fifth industrial revolution; operational technology; smart city; advanced persistent threats; APT; artificial intelligence.
  DOI: 10.1504/IJESDF.2025.10058572
   
  
• Exploring Advanced Steganography Techniques for Secure Digital Image Communication: A Comparative Analysis and Performance Evaluation
  by Rohit Deval, Nachiket Gupte, Johann Pinto, Adwaita Raj Modak, Akshat Verma, Anirudh Sharma, S.P. Raja 
  Abstract: This is a digital age. In a world where everything seems to be public, privacy and confidentiality have never been more important. So, the combination of this aspect of our life and this need of our age is the ability to securely hide data in the digital world in a way where it is not so easy to detect. Thus, the culmination of this thought process helped the authors arrive at the topic of our paper which is steganography in digital images. Image steganography is defined as the process of “concealing a message or piece of data inside an image file”. Image steganography is crucial in the digital era, when the transmission and storage of digital information are widespread, for protecting the confidentiality and integrity of sensitive data. To this end, it has been reviewed in the latest technology and has attempted to put forth the best techniques/algorithms by which data of many kinds can be hidden in digital images. After extensive research, it narrowed down to six techniques which would be presented in this paper.
  Keywords: steganography; digital images; data hiding; encryption; secret message; least significant bit; LSB; steganography; image processing; image compression.
  DOI: 10.1504/IJESDF.2025.10058707
   
  
• Secure System to Secure Crime data using Hybrid RSA-AES and Hybrid Blowfish-Triple DES
  by Raghav Modi, Ananjay Singh Jammoria, Akshat Pattiwar, Arnav Agrawal, S.P. Raja 
  Abstract: Data security is the project’s primary goal. We suggest hybrid cryptography as a technique to keep the data secure. When the sender tries to email the recipient the criminal data, it will be encrypted with a symmetric key utilising symmetric encryption. In most cases, the recipient receives a symmetric key that he can use to decrypt the data. However, we employ hybrid cryptography to further increase security by encrypting the symmetric key using asymmetric encryption and transmitting both the encrypted symmetric key and the encrypted crime data to the recipient. The encrypted symmetric key is decrypted by the recipient using their private key, and the encrypted criminal data is then safely delivered to the recipient using the decrypted symmetric key. In the Police Department data about offenders, including their background, record, and information about their prison records and officers who treated them is maintained in the cloud. Yet, that information is moved from one department to another for departmental purposes. The Hackers may hack this process and contaminate the data. We recommend hybrid cryptography as a strategy to keep this data secure.
  Keywords: Advanced Encryption Standard; AES; Rivest-Shamir-Adleman; RSA; Triple Data Encryption; 3DES; Blowfish.
  DOI: 10.1504/IJESDF.2025.10059534
   
  
• Criminal Protection to the Digital Right to be forgotten in Jordan
  by Mohammad Alshible, Hamzeh Abu Issa 
  Abstract: Individuals’ private information may be readily accessible to third parties, which may exert harm to the individual’s reputation. The so-called Right to be forgotten was established in response to people’s urge to delete information that had previously been published about them. This issue has risen in light of the development of the digital publication. The absence of legal texts that protect this right, and occasionally the existence of legal texts that may contradict this right, such as the right to freedom of expression and the controversy about the practicality of establishing a right to be forgotten, are among the issues that may affect or prevent the enjoyment of this right and its exposure to a violation. The current study will explain the concept and nature of the right to be forgotten and the criminal protection provided by legislation and its problems in Jordanian law.
  Keywords: personal data; right to privacy; right to digital be forgotten; criminal protection of privacy; Jordanian Law.
  DOI: 10.1504/IJESDF.2025.10059697
   
  
• Pixel recurrence based image watermarking for block based integrity verification
  by Radha Ramesh Murapaka, A. V. S. Pavan Kumar, Aditya Kumar Sahu 
  Abstract: This paper proposes a pixel recurrence-based digital image watermarking (PRDIW) scheme to identify the tampered blocks from an image. The proposed scheme obtains two mirrored image (MI) blocks consisting of 2 x2 pixels from each carrier image (CI) pixel block. Next, a one-digit integrity value (ivone) is computed from each block and encoded inside the block to identify the tampered blocks successfully. Additionally, the proposed scheme is reversible. Therefore, it can successfully recover the CI and the encoded watermark bits at the receiving end. The results of the proposed scheme suggest that the quality of the obtained watermarked images (WIs) is superior, with an average peak signal-to-noise ratio (PSNR) of 56.11 dB, 54.68 dB and 53.05 dB, 51.68 dB while watermarking 65,536 and 131,072 bits, respectively. At the same time, the structural similarity index (SSIM) for the entire obtained watermarked image is superior to that of the existing works.
  Keywords: digital image watermarking; integrity; reversibility; watermarked image.
  DOI: 10.1504/IJESDF.2025.10059766
   
  
• Blockchain as an indispensable asset for educational institutions: A systematic review
  by Rabia Nazir, Ahsan Hussain , Syed Zubair Ahmad Shah 
  Abstract: Blockchain is eminently flourishing as an open distributed data structure to record digital transactions efficiently and permanently in cryptographically linked time-ordered sets of blocks. Integration of blockchain into education opens new possibilities by providing a secure platform to share student information, conduct evaluations and track the entire learning process. In this paper, research work on blockchain technology in the education sector is studied thoroughly and systematically organised into groups to get invaluable insight into the implementation of blockchain in education. It focuses on the perquisites of implementing blockchain technology in education as it significantly opens new possibilities for the education sector by providing a secure platform to share student information, conduct evaluations and track the entire learning process. In the education sector blockchain is showing conceptual breakthroughs, however, some administrative and technological concerns are to be addressed. We analysed research articles and found that data management and certificate verification have been two main research themes. This study provides an overview of blockchain applications in the education sector, as a contribution to already existing research work. The characteristics of blockchain used in the previous research are also examined and the research gaps are summarised.
  Keywords: blockchain education; e-certification; information security; smart contracts; IPFS.
  DOI: 10.1504/IJESDF.2025.10059807
   
  
• Methods of Teaching Electronic Administration Legislation by Using Artificial Intelligence Techniques
  by Tareq Al-billeh, Ali Al-Hammouri, Lana Al-Khalaileh 
  Abstract: This qualitative study aims to clarify the role of using artificial intelligence techniques in teaching law subjects to law students and improving the quality of educational services. The research used the descriptive analytical method by analysing the system for integrating e-learning in higher education institutions, analysing the concept of artificial intelligence in the field of e-learning and the role of artificial intelligence techniques in improving the quality of education in law subjects in general and electronic administration legislation in particular. The findings indicated that the use of artificial intelligence techniques in teaching law subjects at the Faculty of Law. It has improved the quality of education about law subjects in general and electronic administration legislation in particular. The need to Faculty of Law should equip lecture halls with the latest artificial intelligence devices and technical and technological equipment to keep pace with recent developments.
  Keywords: teaching methods; artificial intelligence; quality of education; electronic administration legislation; faculty of law; e-learning.
  DOI: 10.1504/IJESDF.2025.10059889
   
  
• Dispute rules applicable to electronic commerce contracts
  by Mohammad Al-Freihat, Abdullah Khsellat, Talal Yassin Aleissa, Galb Shamailah, Ziad Alwahshat, Hashim Balas 
  Abstract: This study aims to explain the applicable legal system, which governs the conduct of transactions and contracts that apply according to the electronic commerce and electronic transactions system, through a statement of the law applicable to the relationship between the two parties under the Jordanian law and the law related to these issues governing electronic contracting operations. The research was carried out from a legal point of view based on the Jordanian laws related to this subject, the Jordanian Electronic Transactions Law, and with reference to the international conventions related to electronic commerce, which are centred on international business operations and international obligations on international electronic commerce, especially the two model laws. The most important findings are that the objective rules governing issues related to international electronic commerce contracts continue to play the role of completeness, allowing them to apply the conflict of laws approach to electronic commerce contracts.
  Keywords: electronic commerce contracts; law applicable; international contracts; Jordanian law.
  DOI: 10.1504/IJESDF.2025.10059959
   
  
• A Proposed model for Security of Quality of Experience (QoE) Data in Cloud Gaming Environment
  by Awais Khan Jumani, Jinglun Shi, Asif Ali Laghari, Aftab Ul Nabi 
  Abstract: Gaming companies heavily rely on collecting quality of experience (QoE) data to gain insights into the performance of their games and make enhancements. This data encompasses aspects such as latency, packet loss and frame rate. By analysing QoE data companies can detect issues with the games infrastructure. Make adjustments to the games design. However, it is important to recognise that QoE data is also vulnerable to attacks. Hackers can exploit vulnerabilities in the games design. Because they are stealing data from QoE data. This unauthorised access can lead to troubles, for players or even compromise personal information of players. In this paper, we have evaluated security measures for online gaming and highlighted the issues related to the interplay between QoE data and data security, security threats, network attacks in multiplayer games, vulnerabilities in QoE data collection, and existing protocols. Moreover, we have proposed a new security measure for QoE data in a gaming environment. Our proposed measure uses encryption, access control, and intrusion detection to protect QoE data from unauthorised access and manipulation. In the last, we have underlined open research issues related to online gaming.
  Keywords: QoE security; online gaming; bidding; subjective QoE; objective QoE; online attacks.
  DOI: 10.1504/IJESDF.2025.10060310
   
  
• An approach towards development of Supervisory Control and Data Acquisition System forensics framework: Concern and Challenges
  by Ramya Shah, Digvijaysinh Rathod 
  Abstract: In the highly competitive technology market, supervisory control and data acquisition/industrial control systems (SCADA/ICS) have seen quick growth. They are also at the heart of operational technology (OT), which is used in businesses and processing facilities to monitor and control crucial processes in varied sectors as energy, railways and many more. However, in the event of a security incident (such as a system failure, security breach, man-in-the-middle attack or denial-of-service attack), it’s critical to comprehend the digital forensics implications of such incidents, the procedures or protocols that must be followed during an investigation, the tools and techniques that an investigator should use, and where and how forensic data can be collected. It is crucial that forensic investigations start right away after a security incident due to the rising threat of sophisticated attacks on key infrastructures. This examination of current SCADA forensic researches and numerous forensic investigation methods is presented in this work. The limitations of employing conventional forensic investigative methods and the difficulties faced by forensic investigators have also been covered. The shortcomings of current research into offering forensic capacity for SCADA systems are also thoroughly reviewed.
  Keywords: SCADA forensics; ICS forensics; OT; digital forensics.
  
  
• A DWT-DCT-SVD Based Robust Watermark Method for Image Copyright Protection
  by PRACHEE DEWANGAN, Debabala Swain, MONALISA SWAIN 
  Abstract: Due to advanced digitisation, the digital contents are easily spread via the internet and cause a lot of copyright concerns. To safeguard the copyright in the source image, watermarking focuses on covertly encrypting hidden data in the main image. This study presents a novel watermarking technique based on discrete wavelet transformation (DWT), discrete cosine transformation (DCT) and singular value decomposition (SVD). The approach first performs multilevel DWT on the cover image before applying the DCT on the low-frequency sub-band. Second, SVD is applied to the watermark image as well as the DCT coefficients of the source image. Thirdly, singular values from SVD operations are used to embed watermark information. In this approach, watermark identification and extraction are successfully done. Compared to the other methods, experimental outcomes show that the proposed method offers better performance in robustness and imperceptibility against different attacks.
  Keywords: image watermarking; discrete wavelet transformation; DWT; discrete cosine transformation; DCT; singular value decomposition; SVD.
  DOI: 10.1504/IJESDF.2025.10060617
   
  
• IoT Security: A Systematic Literature Review of Feature Selection Methods for Machine Learning-based Attack Classification  
  by Jing Li, Mohd Shahizan Othman, Chen Hewan, Lizawati Mi Yu 
  Abstract: In the age of the internet of things (IoT), ensuring security is crucial to protect the interconnected devices and systems. The capacity to identify cyberattacks is essential for IoT security, hence many academics have focused their efforts on developing powerful classification models that can identify intrusions to protect IoT infrastructure. One key factor in creating successful classification models for IoT security is feature selection. To assist researchers and practitioners in selecting the appropriate feature selection methods, this paper presents a systematic literature review of the literature on feature selection approaches for machine learning-based attack classification models in IoT security using IoT datasets. By analysing data from 1272 studies published between January 2018 and December 2022 using preferred reporting items for systematic literature reviews and meta-analyses (PRISMA) guidelines, the review identifies 63 primary studies that meet inclusion criteria. The primary studies are analysed and categorised to answer research questions related to current practices, feature selection methods, benchmark IoT datasets, feature selection validation methods, limitations, challenges, and future directions. The review provides valuable insights for researchers and practitioners seeking to incorporate effective feature selection approaches in IoT security.
  Keywords: internet of things; IoT; feature selection; FS; IoT dataset; attack detection; classification; IoT security; systematic literature review; SLR.
  DOI: 10.1504/IJESDF.2024.10060679
   
  
• A Hybrid Approach for Social Media Forensics
  by Amir Shaker Mahmoud, Ngaira Mandela, Animesh Kumar Agrawal, Nilay Mistry 
  Abstract: Social media plays a pivotal role as a rich source of data for digital forensics investigators, enabling the extraction of valuable evidence for crime analysis. This research introduces a hybrid digital forensics approach for social media investigation, compatible with all web-browsing devices. This hybrid approach combines a three-stage classification process consisting of TextBlob sentiment analysis to analyse the tweet’s polarity, VADER to detect suspicious tweets, and a crime-type dictionary to categorise crime-related tweets. Using Python libraries, Twitter data is collected using authentic Twitter accounts, hashtags, or keywords, then cleaned, translated, geolocated, and classified. A real-time interactive platform is implemented for crime detection and analysis, bolstering the capabilities of law enforcement agencies and researchers in understanding crime patterns. The research concludes with promising results, highlighting the approach’s potential, and discusses future enhancements, ultimately aiding in crime analysis and prevention.
  Keywords: crimes analysis; cybercrimes; digital forensics; hybrid methods; text blob; VADER; social network analysis; Twitter; law enforcement.
  DOI: 10.1504/IJESDF.2025.10060756
   
  
• A Secure SEO Techniques for Improving the Website Ranking: An Efficient Approach
  by Muhammad Abbas, Sarmad Ahmed Shaikh, Rumsia Tahir, Abdullah Ayub Khan, Asif Ali Laghari 
  Abstract: This research paper provides an in-depth exploration of the use of search engine optimisation (SEO) techniques as a means of enhancing a website’s ranking on search engine results pages (SERPs). In the current digital era, where a website’s visibility can make or break its success, SEO has become a crucial component of digital marketing strategies. Therefore, this study focuses on applying SEO strategies to a tech-related website with the aim of achieving a certain level of ranking and targeting specific audiences. They identify several key elements that can improve search results, including on-page optimisation, link building, and keyword research. Therefore, it is essential to employ a holistic approach to SEO that considers various factors, such as the website’s content, structure, and user experience. The paper concludes by examining the concept of optimisation in the context of a particular website, highlighting the importance of continuous monitoring and adaptation of SEO strategies. Overall, they provide valuable insights into the world of SEO and its potential to enhance a website’s visibility and success.
  Keywords: search engine optimisation; SEO; KGR; on-page optimisation; off-page optimisation.
  DOI: 10.1504/IJESDF.2025.10061127
   
  
• A novel method to increase the security in 5G networks using Deep Learning
  by Rajasekar A, Ramamoorthi R., RAMYA M, Vinod A 
  Abstract: Wireless networks are being forced to handle a greater amount of data due to a variety of circumstances, and this trend is progressing at a rapid rate. The Denial of Service (DoS) attacks have the highest rate of growth target the expanding computational network infrastructures all over the globe. As a consequence of this, the objective of this study is to come up with an innovative model for the identification of DoS attacks on 5 G networks. The model will go through two phases: the first will be feature extraction, and the second will be attack detection. In order to successfully carry out the detection, classifiers that have long short-term memories (LSTM) are utilised. The Whale Optimization Algorithm (WOA) model works to optimise the weight of the LSTM. The detected output of a hybrid model that has been trained appropriately provides the accuracy rate of 96.5%
  Keywords: 5G; Deep learning; Dos attacks; Denial-of-service; whale optimization algorithm.
  DOI: 10.1504/IJESDF.2025.10061148
   
  
• The Future of Third Web: A Role of Blockchain and Web 3.0
  by Usman Ali, Irfan Ali Kandhro, Raja Sohail Ahmed Larik, Abdullah Ayub Khan, Muhammad Huzaifa Shahbaz, Muhammad Osama 
  Abstract: Nowadays, people use the web more consistently and the world wide web (www) is used as the largest global information media by this user can write, read or share information throughout the internet. The early web tools were very simple. But with time new tools emerge. The first version of www was web 1.0 which was only static, and the second version of www is web 2.0 users can only read, write and create the data which is help businesses to cover the dynamic data of the users. But the third version of www is web 3.0 use the algorithm which works differently for every user to interpret the individual data and customize the internet for every user. Many companies like YouTube, Netflix, and Spotify use this technology and only share valuable things with users by analyzing their data and behavior.
  Keywords: World Wide Web (WWW); blockchain; Web 3.0; Information System; Risks; Privacy and Security.
  DOI: 10.1504/IJESDF.2025.10061149
   
  
• Legal Regulation of Payment Using Virtual Currency: Comparative Study
  by Nahed Alhammouri, Ali Al-Hammouri, Tareq Al-billeh, Abdulaziz Almamari 
  Abstract: The article analyses the legal regulation of payment using virtual currency, so that virtual currencies such as Bitcoin and Ethereum deal with online payments and transfers directly without relying on traditional financial institutions. The study therefore focuses on the framework for the legal regulation of these currencies and their impact on the economy and consumers. The study includes a review of current legislation in different countries governing the use and trading of virtual currencies. It also examines legal and security challenges related to this type of payment, including fraud, money laundering and funding terrorism. The study highlights the central bank’s and governments’ efforts to regulate and control the use of virtual currencies, including licensing common platforms and exchanges and restricting investment and transfer operations. The study emphasises the need for balanced regulation to achieve legal protection for stakeholders without prejudice to the innovation and development of financial technology.
  Keywords: virtual currency; electronic payment; legal challenges; online transfer; financial technology; international contracts.
  DOI: 10.1504/IJESDF.2025.10061361
   
  
• Secure sensing and computing techniques based on fuzzy in 5G  
  by Ramesh Balasubramani, Kumarganesh Sengottaiyan, Thillaikkarasi Rangasamy, Susaritha Muthusamy , Elango Sellamuthu, Mahaboob Basha Shaik 
  Abstract: The new technology that is employed in 5G applications presents potential security vulnerabilities. In this study, the security needs for 5G applications are analysed, and hierarchical solutions for securing 5G apps are presented for various stakeholders. This study analyses the process of vulnerability assessment in 5G networks and offers an optimised dynamic technique for precisely analysing the vulnerabilities that exist in 5G networks. Specifically, the research focuses on how 5G networks may be attacked. Combining fuzzy numbers with the method for order of preference by similarity to the ideal solution (TOPSIS) is what this approach does. The proposed technique considers both static and dynamic aspects of the 5G network, such as latency and accessibility, to discover the possible attack graph pathways along which an attack may spread across the network. In addition, we compare the enhanced technique to both the classic TOPSIS and the widely used susceptibility scanning application known as Nessus.
  Keywords: fuzzy; security; 5G; encryption algorithms; internet of things; IoT; 5G heterogeneous networks.
  DOI: 10.1504/IJESDF.2025.10061442
   
  
• ANALYSIS OF CYBER DELINQUENCY AMONG ‘GENERATION Z’ IN INDIA  
  by Kiran Shrimant Kakade, Om Astankar Astankar, Anjali Kulkarni, Jayant Brahmane, Sulakshana B. Mane, Poonam Nathani 
  Abstract: Misuse of digital platforms by people especially at tender age and their behaviour in the virtual world is a cause of concern for various regulating agencies. This study gathers the most recent and available evidences about the relation between behavioural aspects of people in cyber world along with their inclination towards cyber-crime. It was specifically created to understand the demographic characteristics that contribute to cybercrime through a variety of mixed analytical methodologies and the incorporation of theoretical frameworks from criminology and psychology, including cyber psychology and computer science. The possibility that young people with an interest in technology could develop into cyber-juvenile offenders, lone cyber-criminals, and organised cyber-criminals was taken into account. Understanding whether demographic factors also influences cybercrime is essential for developing effective prevention and intervention strategies. The results of the study are expected to provide guidance to the regulating agencies as well as other organisations to design measures for preventing cyber-crime and ensure safety of their system and processes from the danger of cyber criminals.
  Keywords: cybercrime; juvenile delinquency; generation Z; cyber world; iGeneration; India.
  DOI: 10.1504/IJESDF.2025.10061443
   
  
• DarkExtract: Tool for Extracting and Analyzing Tor browser host-based Activities  
  by Ngaira Mandela, Amir Shaker Mahmoud, Animesh Kumar, Nilay Mistry 
  Abstract: The increasing usage of Tor Browser, a popular tool for anonymous web browsing, has presented unique challenges for forensic investigators in analysing digital evidence. This research paper introduces Dark_Extract, an open-source tool designed to simplify the identification and analysis of host-based artefacts left by Tor browser. The purpose of this study is to address the challenges associated with forensic analysis of Tor Browser traces by providing a user-friendly and efficient solution. The methodology employed in developing Dark_Extract involved the analysis of Tor Browser’s architecture and the identification of key host-based artefacts relevant to forensic investigation. The tool was then developed to automate the extraction and analysis of these artefacts, eliminating the need for extensive knowledge of Tor Browser’s intricate structure. The major findings of this study demonstrate the effectiveness of Dark_Extract in simplifying the forensic analysis of Tor Browser traces. The tool successfully extracts and presents crucial host-based artifacts such as downloads, cookies, browsing history, and bookmarks, which can be of significant importance in forensic investigations. The results obtained through the use of Dark_Extract indicate its accuracy and efficiency in identifying and organising these artefacts.
  Keywords: Tor browser; dark web; dark net; forensic investigation; digital evidence; host-based artefacts; anonymous web browsing; forensic data extraction.
  DOI: 10.1504/IJESDF.2025.10061873
   
  
• VLMDALP: Design of an efficient VARMA LSTM based Model for identification of DDoS attacks using Application-Level Packet analysis  
  by Meghana Solanki, Sangita Chaudhari 
  Abstract: Identification of distributed denial-of-service (DDoS) attacks at the application level in networks is a multimodal task that involves the analysis of various network parameters, including packet signatures, node-level analysis, and traffic patterns. Existing attack detection models are highly complex or do not support multiple attack scenarios. To address these issues, we propose an efficient hybrid model that combines the Vector Autoregressive Moving Average (VARMA) with Long Short-Term Memory (LSTM) techniques for identifying DDoS attacks through application-level packet analysis. The proposed model initially employs the Vector Autoregressive Moving Average Model to extract hierarchical features from raw packet data across multiple domains, including time, frequency, and spatial domains. These learned features are then enhanced using the LSTM model. The combined features create a concise and informative representation of the packets, which is fed into a fully connected neural network for classifying multiple types of attacks. To evaluate the effectiveness of our proposed model, we conducted experiments on real-world network datasets, including samples from the Application- Layer DDoS Dataset.
  Keywords: Network Forensics; Attacks; Analysis; Application Layer DDoS; VARMA; LSTM; Samples.
  DOI: 10.1504/IJESDF.2025.10061885
   
  
• Advanced forensic analysis of Tails Operating Systems and its implication to cybercrime in Deep and Dark web  
  by Ngaira Mandela, Amir Shaker Mahmoud, Animesh Kumar, Nilay Mistry 
  Abstract: The Tails operating system, renowned for its emphasis on privacy and anonymity, has become a preferred choice for individuals seeking to safeguard their online activities. Tails OS design centres around providing a secure environment that leaves minimal traces, thereby attracting privacy-conscious users. However, this very secure design also entices cyber criminals operating in the digital landscape, to use Tails to perpetrate illicit activities, creating new challenges for digital forensics practitioners in their pursuit of extracting evidence. This paper conducts an exhaustive forensics of Tails operating system, aiming to uncover the digital remnants left behind during its utilisation. By analysing the RAM, network, disk, and virtualisation, employing a range of activities and forensic tools, many artefacts are unearthed that provide insight into user interactions within the Tails environment. This research contributes to our understanding of the interplay between privacy preservation and digital evidence recovery, shedding light on the complexities of investigating a privacy-focused operating system like Tails.
  Keywords: Tails operating system; amnesic incognito live system; privacy-focused OS; anonymity; cybercrime; digital forensics; deep and dark web.
  DOI: 10.1504/IJESDF.2025.10061945
   
  
• Forensic Investigation and Analysis of Malware in Windows OS  
  by Frank Fiadufe, Krishna Modi, Kapil Shukla, Felix O. Etyang 
  Abstract: Malware has become a pervasive concern for malware analysts and digital forensics. This research investigates malware forensics to detect, investigate, and analyse malicious software. The research examines the application of digital forensic science to dissect threat vectors, specifically malware, shedding light on their behaviour on computer hard disks and memory. Using various digital forensic tools, memory forensics, and harddisk forensics are performed on an infected Windows 7 OS, followed by static and dynamic analysis of malicious software. Memory samples are analysed using volatility for memory forensics, while disk images are analysed using autopsy for harddisk forensics. The malware’s functionality is fully comprehended through meticulous extraction and analysis. A robust framework for malware forensic investigation emerges, facilitating detection, analysis, and understanding of malware behaviour. This research underscores the significance of integrating digital forensics tools and techniques to combat evolving malware threats effectively.
  Keywords: digital forensics; memory forensics; harddisk forensics; static and dynamic analysis.
  DOI: 10.1504/IJESDF.2025.10062010
   
  
• Network Security Attack Classification: Leveraging Machine Learning Methods for Enhanced Detection and Defense.  
  by Irfan Ali Kandhro, Ali Orangzeb Panhwar, Shafique Ahmed Awan, Raja Sohail Ahmed Larik, Abdul Ahad Abro 
  Abstract: The rapid growth and advancement of information exchange over the internet and mobile technologies have resulted in a significant increase in malicious network attacks. Machine learning (ML) algorithms have emerged as crucial tools in network security for accurately classifying and detecting these attacks, enabling effective defence strategies. In this paper, we employed ML methods such as logistic regression (LG), random forest (RF), decision tree (DT), k-nearest neighbours (KNN), and support vector machines (SVM) for building an intrusion detection system using the publicly available NSL-KDD dataset. Our proposed method utilised feature engineering and selection techniques to extract relevant features. We trained classification models and optimised their parameters using cross-validation and grid search techniques. The models exhibited robustness in identifying unseen attacks, enabling proactive defence strategies. In this paper, we contribute to the field of network security by showcasing the efficacy of machine learning methods, empowering organisations to enhance their defences and respond to threats promptly. Future research can explore advanced models and real-time monitoring techniques to develop dynamic defence mechanisms.
  Keywords: attacks classification; network security; cyber security; machine learning; adversarial attacks.
  DOI: 10.1504/IJESDF.2025.10062253
   
  
• A model for detecting cyber security intrusions using machine learning techniques  
  by Leo John Baptist, Janani Selvam, Divya Midhun Chakkaravarthy 
  Abstract: Because hackers are using more sophisticated methods, the number of cyberattacks is rising at an alarming rate. In addition, maintaining adequate levels of cyber security is becoming more difficult on a daily basis due to the prevalence of malicious actors carrying out cyberattacks in the modern digital environment. Therefore, in order to have a safe network, it is required to establish privacy and security measures for the systems. A substantial amount of further research is required in the domain of intrusion detection. This study introduces an intrusion detection tree (referred to as ‘IntruDTree’), which is a security model based on machine learning. Ultimately, the efficacy of the IntruDTree model was assessed by the execution of tests on many cybersecurity datasets. To assess the efficacy of the resulting security model, we conduct a comparative analysis between the outputs of the IntruDTree model and those of other well-established machine learning techniques.
  Keywords: cybersecurity; cyber-attacks; anomaly detection; intrusion detection system; machine learning; ML.
  DOI: 10.1504/IJESDF.2025.10062655
   
  
• Artificial intelligence and security: some reflections concerning the freedom of expression, information and democracy  
  by Federico Fusco 
  Abstract: In contemporary society, the role of information in socio-economic development is increasing across domains such as policy, business, technology, and society. Among sources of information, news hold significant sway in shaping public opinion. However, the proliferation of fake news presents a significant threat to many societies, particularly when they are part of disinformation campaigns orchestrated by hostile actors. While disinformation campaigns are not a new phenomenon, the ease and speed of spreading false information via the internet and social media, as well as advancements in artificial intelligence that enable text generation resembling human language, have made them a growing concern. In light of these developments, this paper provides a comprehensive examination of the challenges posed by fake news, with a particular focus on the role played by artificial intelligence.
  Keywords: artificial intelligence; fake news; freedom of expression; disinformation; media law; democracy.
  DOI: 10.1504/IJESDF.2025.10062899
   
  
• IoT security using deep learning algorithm: intrusion detection model using LSTM  
  by Abitha V. K. Lija, R. Shobana, J. Caroline Misbha, S. Chandrakala 
  Abstract: Internet of things (IoT) and the integration of many gadgets is rapidly becoming a reality. IoT devices, particularly edge devices, are particularly vulnerable to cyberattacks as a result of the proliferation of device-to-device (D2D) connectivity Advanced network security measures are required to do real-time traffic analysis and to mitigate malicious traffic. These mechanisms must also be able to detect malicious traffic. We describe a game-changing approach to detect and classify new malware in record time. This will allow us to handle the difficulty that has been presented (zero-day malware). This article puts out the idea of a hybrid deep learning (DL) model for the detection of cyber attacks. Long short-term memory (LSTM) and gated recurrent unit are the foundations of the model that has been suggested (GRU). The results of the experiments are quite encouraging, revealing an accuracy rate of 94.50% for the identification of malware traffic.
  Keywords: deep learning; gated recurrent units; internet of things; IoT; long short-term memory; LSTM; machine learning.
  DOI: 10.1504/IJESDF.2025.10063216
   
  
• A novel scalable and cost efficient blockchain solution for managing lifetime vaccination records based on patient preference  
  by Neetu Sharma, Rajesh Rohilla 
  Abstract: This study aims to design a novel, cost-efficient blockchain-based solution for managing lifetime vaccination records based on patient preference. The proposed design reduces fraud in vaccination certification by providing QR code-based validation. The proposed system stores the cryptographic-hash of vaccination certificates on the blockchain for security and integrity validation. For scalability, availability, and store-house cost reduction, vaccination records have been stored off-chain through a private interplanetary file system (IPFS) based on patient preference. The smart contract is successfully deployed and tested over the Remix IDE environment. Performance has been evaluated by analysing execution costs at different transaction sizes. Moreover, we have evaluated the probability of data availability for a private IPFS network, which was not done in any previous work. Furthermore, we have analysed the network parameters to get optimal data availability at a low storage cost. The comparative analysis proves that the proposed scheme is better than existing schemes.
  Keywords: blockchain; vaccination; security; IPFS; scalable; Ethereum; smart contract.
  DOI: 10.1504/IJESDF.2025.10063362
   
  
• A proposed framework for the detection of cyber threats using open-source intelligence tools in real-time  
  by Ravi Sheth, Chandresh Parekha 
  Abstract: In today’s fast-changing digital world, getting accurate and relevant information is crucial for cybersecurity, competitive analysis, and research. With open-source intelligence (OSINT) technology, social media and digital platforms have become significant data sources. Our research will compare different OSINT systems for data collection. We’ll examine various OSINT tools, categorise them based on functionality, usability, and effectiveness, including web scrapers, social media analytics, domain analysers, and search engines. Each tool will undergo evaluation based on data collection capabilities, result accuracy, module availability, and user-friendliness. It aims to assist individuals - be they sufferers, academics, or practitioners in making informed decisions when selecting open-source intelligence (OSINT) tools for information extraction. We have provided a detailed comparison of their capabilities and limitations, along with ethical considerations, for using OSINT tools effectively and responsibly in various applications and the proposed cyber threat alert system in this paper.
  Keywords: template OSINT; information gathering; web scanner; search engine; cyber security.
  DOI: 10.1504/IJESDF.2025.10064154
   
  
• Image encryption and decryption using graph theory  
  by Selva Kumar, Saravanakumar Chandrasekaran, Nalini Manogaran, Bhadmavadhi Krishna 
  Abstract: Through the application of the ideas presented in graph theory, this work presents a novel approach to the protection of picture data. The approach that has been developed takes into account the pixels that make up the digital picture as vertices of a network and forms edges between the vertices, assigning a certain amount of meaningful weight to each connection. The encryption and decryption procedure for the colour digital picture is presented. This approach makes use of the minimum spanning tree (MST) and the weighted adjacency matrix of the MST. For the purpose of validating the practicability and robustness of the suggested approach, the experimental findings and the security analysis of the proposed methodology are presented. In order to demonstrate that the suggested approach is resistant to statistical assaults, statistical analysis techniques such as histogram, correlation, and entropy were used. It has also been shown via the results of the experiments that the suggested method is resistant to assaults including brute force and occlusion.
  Keywords: image encryption; graph theory; encryption phase; computation results.
  DOI: 10.1504/IJESDF.2025.10064183
   
  
• Adversarial attacks on machine learning-based cyber security systems: a survey of techniques and defences  
  by Pratik S. Patel, Pooja Panchal 
  Abstract: Machine learning (ML) has been increasingly adopted in the field of cyber security to enhance the detection and prevention of cyber threats. However, recent studies have demonstrated that ML-based cyber security systems are vulnerable to adversarial attacks, in which an attacker manipulates input data to deceive the ML model and evade detection. This paper presents a survey of adversarial attacks on ML-based cyber security systems, including techniques such as evasion, poisoning, and backdoor attacks. Additionally, we discuss the limitations of current defences against adversarial attacks, such as defensive distillation and adversarial training, and propose potential future directions for defence mechanisms. Finally, we provide a framework for evaluating the effectiveness of existing defences against adversarial attacks on ML-based cyber security systems. Our survey highlights the urgent need for developing more robust and reliable defence mechanisms to ensure the security and reliability of ML-based cyber security systems in the face of adversarial attacks.
  Keywords: attacks; machine learning; ML; cybersecurity; evasion; threat.
  DOI: 10.1504/IJESDF.2025.10064222
   
  
• An intelligent method for detection and classification of Darknet traffic using sequential model along with Adam and stochastic gradient decent optimisers  
  by Ravi Sheth, Chandresh Parekha, Kinjal Sheth 
  Abstract: The clandestine nature of darknet activities poses a significant challenge to traditional cybersecurity measures, necessitating advanced techniques for effective detection and classification. Darknet traffic classification is very much needed now days as day by day the market of illegal and hidden services are being increased in the darknet. There are various machine learning-based approach has been proposed for the categorisation of darknet traffic but very few work has been done using the concept of deep learning. This research introduces an intelligent approach which leverages a sequential deep learning model to enhance the accuracy and efficiency of darknet traffic detection and classification. In the training phase, the model is exposed to a diverse dataset encompassing a wide range of darknet traffic patterns, ensuring its ability to generalise and recognise novel patterns in real-world scenarios. The proposed model has used sequential model along with the stochastic gradient descent (SGD) and Adam optimiser which successfully detect and classify the darknet traffic with the overall accuracy of 96.77%.
  Keywords: sequential model; Adam; stochastic gradient decent; SGD; darknet traffic; detection; classification.
  DOI: 10.1504/IJESDF.2025.10064345
   
  
• On classifying memory contents at page-level granularity: machine-learning approach  
  by Mohammed I. Al-Saleh, Akram Alkouz, Abdulsalam Alarabeyyat, Majed Bouchahma 
  Abstract: A significant challenge faced by digital investigators in the realm of law is performing digital media triage, which involves determining the relevant data that may aid in a criminal investigation. Effective triage can save time and improve investigative outcomes, particularly in memory investigation, as its contents are often scattered and diverse. Identifying and classifying file types in memory can be difficult due to the way the operating system’s paging scheme maps file contents into non-consecutive page frames in physical memory. This paper presents a machine learning approach to triage memory content at the page level, focusing on the classification of common file types within the context of law. The study conducted various experiments, and the results indicate that it is possible to accurately classify in-memory data into primary file categories, thus contributing to the field of digital investigation in accordance with legal processes.
  Keywords: memory forensics; digital media triage; machine learning; file type detection; digital forensics; classifying memory content.
  DOI: 10.1504/IJESDF.2025.10064346
   
  
• Enhancing network security: a deep learning-based method to detect and diminish attacks  
  by R.E. Franklin Jino, Arockia Mary Paulsamy, Gobinath Shanmugan, Rajesh Kumar Vishwakarma 
  Abstract: In today’s world, preventing illegal intrusions into communication networks is an absolute need in order to protect the personal information of users and maintain the integrity of their data. The establishment of intrusion detection systems (IDS) and the improvement of their accuracy have both been shown to benefit from the use of data mining and machine learning techniques. We make use of the well-known AWID3 dataset, which contains traffic from wireless networks. The Krack and Kr00k attacks, which are aimed at the most serious vulnerabilities in the IEEE 802.11 protocols, are the primary focus of our research and development efforts. This success rate was reached by our ensemble classifier. When it came to identifying instances of the Kr00k attack, our neural network-based model had a high accuracy rate of 96.7%, which further emphasised the usefulness of the remedy that we suggested.
  Keywords: wireless; IDS; machine learning; Krack; Kr00k; IEEE8021.11.
  DOI: 10.1504/IJESDF.2025.10064402
   
  
• Exploring novel encryption approaches for safeguarding heterogeneous data  
  by Dhruvi Karansinh Zala, Mohammad Akram Khan 
  Abstract: In today’s global landscape, data security faces complex challenges due to diverse data forms and transmission structures. This study explores safeguarding data through unconventional encryption methods, stressing the need for adaptable techniques. It begins by assessing traditional encryption’s performance with diverse data types, highlighting security gaps in uniform encryption. The research advocates for encryption that handles data complexities, proposing strategies aligned with heterogeneous data’s variability. Practical tests and simulations inform these novel encryption algorithms, aiding in advancing security protocols for diverse scenarios. Ultimately, this research contributes to evolving data security in heterogeneous environments, emphasizing the importance of robust encryption in managing varied data formats and structures in an interconnected world.
  Keywords: encryption; data security; image; audio; video; text; heterogeneous data; privacy preservation; hybrid encryption; multi-level encryption.
  DOI: 10.1504/IJESDF.2025.10064447
   
  
• Machine learning-based cyber attack recognition model  
  by Leo John Baptist, Janani Selvam, Divya Midhun Chakkaravarthy 
  Abstract: Internet plays an essential role in the daily lives of individuals living in the contemporary world. Because of the volume of users, our private information runs the risk of being disclosed inadvertently somewhere else on the internet. The study of cyber security encompasses a wide range of topics, the most basic of which are the abuse of data and risks to internet security. The proposed system that performs an analysis of the dataset and determines if the data in question is typical or out of the ordinary. Following the completion of the dataset analysis, make an effort to recognise and forecast a cyber attack. The ensemble classification approach is used to determine the attack wise detection accuracy found by CADM. The categorisation of network traffic data has been done with the help of the gradient boosting and random forest algorithms. We achieved an accuracy level of 97.4%.
  Keywords: cyber attack detection; deep machine learning; DML; smart power grid; data processing.
  DOI: 10.1504/IJESDF.2025.10064475
   
  
• Exploring machine learning techniques for detecting anomalies in digital forensics: a survey  
  by Khawla Almutawa, Afef SELMI, Tarek Moulahi 
  Abstract: Cybercrime has recently and rapidly increased as a result of the extensive use of various digital devices. Digital forensic science, which was established to address issues of cybercrime, follows a defined approach to gathering digital evidence. In recent years, there has been a growing number of studies focusing on employing machine learning and deep learning in digital forensics applications. This research is motivated by the increasing importance of digital forensics and cybersecurity and the need for accurate and efficient tools to detect and mitigate potential security breaches and other forms of anomalous behaviour in digital systems. The purpose of this study is to conduct a literature review to obtain a comprehensive understanding of this topic, specifically deployed models, data preprocessing mechanisms, anomaly detection techniques, and evaluations. This review will help to identify gaps in the existing knowledge and potentially uncover new approaches to the topic. This review conducts a comprehensive literature review on anomaly detection in log files using ML and DL techniques. It will help to identify gaps in the existing knowledge and potentially uncover new approaches to the topic. Initial results indicate that deep learning methods show promise in effectively dealing with the intricate characteristics of log data.
  Keywords: digital forensics; DF; machine learning; ML; anomaly detection; log files.
  DOI: 10.1504/IJESDF.2025.10064574
   
  
• Deep learning-based anomaly detection in video surveillance  
  by C. Rajesh, B. R. Tapas Bapu , S. Asha , Ravi Kishore Veluri 
  Abstract: In this day and age of smart cities, the use of video monitoring has assumed a position of critical significance. Large numbers of surveillance cameras have been installed in public and private locations for the purpose of monitoring the properties of infrastructure and ensuring the safety of the general public. This research provides a multi-modal CNN-BiLSTM autoencoder framework for detecting anomalous events in important surveillance environments such as bank ATMs. The approach is built on semi-supervised deep learning and uses multi-modal data. In addition, because there was no publicly accessible dataset for ATM surveillance, we created a one-of-a-kind RGB+D dataset specifically for this purpose. This was done because there was no dataset for ATM surveillance in the public domain. The proposed methodology is validated by testing it on the RGB+D dataset that was collected as well as two other real-world benchmark video anomaly datasets: Avenue and UCFCrime2Local.
  Keywords: video surveillance; security; deep learning algorithm; anomaly detection.
  DOI: 10.1504/IJESDF.2025.10064692
   
  
• A supervised machine learning based framework for deanonymisation of blockchain transactions  
  by Rohit Saxena, Deepak Arora, Vishal Nagar 
  Abstract: As a cryptocurrency, Bitcoin serves as a decentralised ledger for recording transactions. The owner of a Bitcoin keeps their identity secret and hides it behind a special address known as a pseudonym. Because Bitcoin offers anonymity, it has evolved into the favoured option for cybercriminals involved in illegal activities. In this research, supervised machine learning has been used to propose a framework for identifying anonymous user activities on the Blockchain. A labelled dataset containing transactions has been created as a training dataset to carry out the classification of user activities. The fundamental objective is to classify Blockchain transactions to deanonymise them and separate unethical from ethical ones. Synthetic minority oversampling technique (SMOTE) and weight of user activities were used to address the issue of class imbalance. On the samples from the class imbalanced and class balanced datasets, k-nearest neighbours (KNN) exhibited outstanding cross-validation accuracy with default parameters and hyperparameters.
  Keywords: Bitcoin; deanonymisation; supervised machine learning; classification; k-nearest neighbours; KNN; decision trees.
  DOI: 10.1504/IJESDF.2025.10064694
   
  
• AI chatbots: security and privacy challenges  
  by Manju Lata, Vikas Kumar 
  Abstract: The increasing use of artificial intelligence (AI) chatbots in different domains has surely developed the competence, customer capabilities and engagement. On the other hand, the AI chatbots regularly handle sensitive data, making them striking objects on behalf of the malicious actors. Present work describes the prominent security and privacy issues related to the use of AI chatbots, in order to plan the mitigation strategies. An all-inclusive approach is required to handle the security and privacy issues to ensure the transparent data practices, robust security techniques, ethical improvement procedures, and compliance with significant regulations. Since the AI chatbots continue to advance at a faster pace, a pre-emptive approach is crucial for reliable and protected amalgamation into sustainable digital lives. By probing and mitigating security and privacy challenges, this paper contributes to the reliable development and deployment of AI chatbots, with adoption of more secure and trustworthy conversational AI environment.
  Keywords: AI chatbots; security; privacy; data security; mitigation strategies; challenges; AI environment.
  DOI: 10.1504/IJESDF.2025.10065035
   
  
• Data security by means of cryptography and image processing - a deep learning based method  
  by Pooja Sharma, Anurag Patel 
  Abstract: Traditional picture encryption methods make use of rounds of diffusion and confusion in order to create an explicit trade-off between the degree of security and the length of time it takes to decode an image. Deep learning is a method that has the potential to be used in the production of responses that are acceptable for present problems in photo encryption systems. We explore the advantages and disadvantages of each of these research approaches and develop conclusions that are relevant to our findings. In the second step of the process, the various methods are compared and analysed in terms of the cryptographic properties of the recovered photos and the quality of the cipher images that are produced. Finally, conclusions are formed from comparing and analysing the deep learning technique in end-to-end encryption and decryption systems. These findings serve as a platform for future research.
  Keywords: deep learning; image encryption; cryptographic attacks; encryption keys; style transfer.
  DOI: 10.1504/IJESDF.2025.10065036
   
  
• Matrix-based homomorphic encryption-using random prime numbers  
  by Sonam Mittal, Ketti Ramachandran Ramkumar 
  Abstract: Cloud service providers (CSPs) provide security to data during communication and storage, but data security during computation remains a challenge. Homomorphic encryption facilitates the computation of the encrypted data to guarantee the confidentiality and security of the client’s data. Most of the existing homomorphic encryption schemes work on bit-level plaintext and have various issues, such as large ciphertext size, impractical key size, noise growth, and more computational overhead. etc. The paper presents a homomorphic encryption algorithm based on integer numbers which uses the camouflage process and a matrix, filled with random prime numbers to transform the original plaintext to the next prime number and to have a more secure encryption scheme with reduced computational and memory overheads. The comparative analysis shows the memory requirement, ciphertext size, and key length as 4,896 bytes, 16.7 digits, and 3.6 digits for variant 1 and 4,855 bytes, 17.1 and 3.5 digits for variant 2 are less than existing standards.
  Keywords: homomorphic encryption; security; random prime numbers; substitutional matrix; camouflage process; cloud service providers; CSPs.
  DOI: 10.1504/IJESDF.2025.10065110
   
  
• Enhancement of data security in wireless sensors network: application in internet of things  
  by K. Sathiya Priya, C. Rajabhushanam 
  Abstract: As wireless sensor networks (WSNs) expand their application areas and become more widely utilised, the need for security in these networks becomes unavoidable and essential. The fundamental properties of wireless sensor networks do, however, impose limits on sensor nodes. These constraints include limited energy, processing capabilities, and storage capacity, among other things. The purpose of this article is to offer an outline of the problems about privacy and security that are linked with sensor networks. It has been shown that asymmetric key cryptography is not suitable for wireless sensor networks. However, in light of the development of new energy-efficient sensor nodes such as TelosB and others, researchers are exploring and assessing the influence that public key cryptography has on wireless sensor networks. At the time of data transmission, the proposed work achieves a 9.67% improvement in security, and it achieves an 11.38% improvement in security.
  Keywords: secure data aggregation in WSN; concealed data aggregation; homomorphic encryption.
  DOI: 10.1504/IJESDF.2025.10065111
   
  
• Image encryption using artificial intelligence algorithms for secure communication  
  by S. Kumarganesh, D. Jennifer, B. Ramesh, S. Elango, A. Gopalakrishnan 
  Abstract: Within the context of today’s networks and massive amounts of data, the safe transmission of digital photographs is met with a myriad of formidable obstacles. We provide an adaptable framework with the goal of maintaining the privacy and safety of photographs that are delivered over an electronic healthcare system. In our technique, the 3D-chaotic system is used to produce a keystream, which is then applied to the picture to achieve 8-bit and 2-bit permutations respectively. The efficacy of the proposed encryption method is shown using histogram analysis, neighbouring pixel correlation analysis, anti-noise attack analysis, and resistance to occlusion attack analysis. The technique of encryption offers a number of benefits, including a high volume of information that may be encrypted, strong resilience, and a quick decryption time. This demonstrates that the scheme is capable of withstanding statistical attacks and suitable for use as a security framework in AI-based healthcare.
  Keywords: image encryption; AI; healthcare; secure communication.
  DOI: 10.1504/IJESDF.2026.10065276
   
  
• Constitutional Protections in Utilising Artificial intelligence Systems for Investigating and Inferring Crimes: A Comparative Study  
  by Ashraf Fathi Al-Rai, Nayel Musa AlOmran 
  Abstract: This paper explores the potential use of artificial intelligence AI) systems in investigating and deducing crimes and examines their impact on constitutional and legislative guarantees. With the rise of AI entities committing crimes using advanced systems. However, it can be used like any other technical system in the investigation and reasoning process. The problem arises in how to apply constitutional guarantees to the accused when AI systems investigate and deduce the crime. The paper reveals that there are no constitutional or legislative texts in Jordan related to the use of AI systems during the investigation and inference phase of crimes. Although many countries use AI systems for data and information collection, such as the United States of America, Britain, Germany, and the United Arab Emirates, they have not yet used them legally in the inference, investigation, and investigation processes through AI itself.
  Keywords: constitutional; guarantees; artificial intelligence; investigation; reasoning; crimes.
  DOI: 10.1504/IJESDF.2026.10065277
   
  
• Improving reliability with wormhole detection for mobile routing to enhance network security  
  by V. Vidya Lakshmi , B. Akash , M. Manesh , A. Praveen , V. G. Rohith  
  Abstract: Problems with mobile routing in lossy, low-power networks (LLNs) are examined in this paper The IPv6 routing protocol for low-power and lossy networks (RPL), which is the IPv6 standard routing protocol for LLNs, has mostly been investigated in static LLNs and does not clearly contain a mobility support mechanism The IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) protocol, which aims to make it possible to transmit IPv6 packets over low-power wireless networks, is one example of an LLN protocol Routing Protocol for Low-Power and Lossy Networks (RPL) and Constrained Application Protocol are other LLN protocols (CoAP) A node in an ad hoc network collects packets from one location and retransmits them to another using a long-distance link inside the network This method determines the wormhole link by computing the largest end-to-end latency between any two nodes in the communication range.
  Keywords: RPL; LLN; hop distance; RSSI; routing protocols.
  DOI: 10.1504/IJESDF.2025.10065316
   
  
• Deep neural network for the purpose of developing an intrusion detection system for wireless sensor networks  
  by Swagata Sarkar, B. V. Santhosh Krishna , D. Chithra , Sheshang Degadwala 
  Abstract: A wireless sensor network is composed of a large number of sensor nodes, which collects data and transmits it to a centralised location. They have a lot of security problems, though, because nodes have limited resources. Network intruder monitoring systems do these things for the network, and any information network has to have them. Techniques from the field of machine learning are often used in breach detection systems. Based on the findings of this research, a deep neural network-based intruder detection system was proposed as a solution to this issue and an improvement to performance. Intrusion detection systems can be very helpful in finding and stopping security threats. One way to solve this problem is to use the intrusion detection system with more efficient methods that can choose the best route at every point. The proposed method shows an accuracy of 97.5%.
  Keywords: deep neural networks; DNN; deep learning; DL; wireless sensor networks; WSN; base station; BS; intrusion detection system; IDS.
  DOI: 10.1504/IJESDF.2025.10065369
   
  
• Application of audio-video data embedding approach to increase imperceptibility and robustness using forensic detection  
  by Sunil K. Moon 
  Abstract: Data hiding using steganography plays a very important role in providing security, privacy, authentication, and robustness of secret data. In today’s digital world, the significance of safeguarding data privacy and its authentication is very crucial and risky. There are many ways to protect and safeguard the security, and privacy of secret data using steganography techniques but, all this steganography approaches are used to embed secret data like images, text, and audio which provide less privacy, and data security. This paper implemented the multi pixel exploiting modification direction (MP-EMD) approach on video where three pixels of any frames are used to embed secret data at a time using forensics detection technique. The simulated and verified results verify the better privacy and safeguarding of the secret data, peak signal noise ratio (PSNR), correlation indicator (CI), robustness, and embedding capacity (EC) as compared to any existing methods.
  Keywords: safeguarding; security; privacy; robustness; MP-EMD; forensic detection; peak signal noise ratio; PSNR.
  DOI: 10.1504/IJESDF.2025.10065780
   
  
• Cloud computing's multi-key privacy-preserving deep learning system  
  by A. Mani , M. Shanmuganathan , R. Babitha Lincy , J. Jency Rubia  
  Abstract: Many fields have seen success with deep learning implementations, including bioinformatics, photo processing, gaming, computer security, etc. However, a large amount of training data is typically required for deep learning, which may not be made available by a single owner. As the amount of data continues to rise at an exponential rate, many people are turning to remote cloud services to store their information. Human activity recognition (HAR) provides massive amounts of data from IoT devices to collaboratively construct predictive models for medical diagnosis. To protect users’ anonymity in scenarios where DNNs are used in HAR learning, we present Multi-Scheme Differential Privacy. MSDP uses a multi-party, secure variant of the ReLU function to cut down on transmission and processing time. MSDP is proved to be secure in comparison to existing state-of-the-art models without compromising privacy through experimental validation on four of the most popular human activity detection datasets.
  Keywords: internet of things; IoT; multi-key privacy-preserving; deep learning.
  DOI: 10.1504/IJESDF.2025.10065782
   
  
• Image encryption using deep learning : application of AI in medical Images  
  by Ravi Kishore Veluri , Sulakshana B. Mane, V. Sureka , K. Gokulkannan  
  Abstract: The Fourier frequency domain provides the opportunity to differentiate between the dominant frequency of each collection of pictures. After each group is stacked on top of the others, the ciphertext is scrambled. This process is repeated until the final ciphertext is constructed. Throughout the whole decryption process, deep learning is used in order to improve the speed at which the decryption process is carried out and the quality of the recovered image. In particular, the ciphertext that has been retrieved may be sent into the neural network that has been trained, and after that, the plaintext image can be immediately recreated. The results of experimental study indicate that the CC of the decrypted output may be more than 0.99 when 32 photos are encrypted by the process.
  Keywords: optical information security; deep learning; sinusoidal coding; frequency multiplexing.
  DOI: 10.1504/IJESDF.2026.10065826
   
  
• Advanced intelligent data hiding using video stego and hybrid convolutional neural networks  
  by Ravi Kumar  
  Abstract: The practice of steganography involves the concealment of confidential data inside other, seemingly innocuous files of the same or other sorts. The objective of this study is to create a stego technique that, when applied to a video clip, will successfully conceal a message inside its graphics. A model is developed for video steganography by developing a model to conceal video inside another video using hybrid convolutional neural networks (HyCNN). The second objective is to expand the size of the space that can be used for hiding, which has been accomplished via the use of CNN. The suggested model was trained using HyCNN on arbitrary pictures drawn from the ImageNet database. The findings also show that the system is able to produce excellent results in visibility and attacks, where the suggested approach is able to effectively mislead both the observer and the steganalysis software.
  Keywords: convolutional neural networks; hiding data; image stego; steganography; video stego.
  DOI: 10.1504/IJESDF.2026.10066025
   
  
• Securing wireless sensor networks using machine learning and blockchain  
  by K. Sathiya Priya , C. Rajabhushanam 
  Abstract: This study studies the prospect of improving the reliability and security of a Wireless Sensor Network (WSN) by using blockchain technology. The process starts with the gathering of routing data via the use of Q-tables in MATLAB. This is followed by the uploading of the data to the blockchain, where it is encrypted using SHA256 cryptography to ensure its safety. Proof of Work (PoW) and Proof of Authority (PoA) are the two consensus algorithms used to evaluate the robustness of the blockchain network. We carry out a Sybil attack on the blockchain network with the intention of determining how effective Proof of Work and Proof of Authority are in identifying and preventing breaches of security. The evaluation demonstrates that Proof-of-Work (PoW) offers greater security assurances, preserving the integrity, validity, and resilience of the blockchain against the attack, despite the fact that it requires more computing resources.
  Keywords: wireless sensor network; WSN; machine learning; blockchain; agriculture; security.
  DOI: 10.1504/IJESDF.2026.10066091
   
  
• Digital distinctiveness - a study and review on the methods that prevent identity hacking in metaverse  
  by Lakshmi Mansi Chada, S.P. Raja 
  Abstract: Digital identity is the most crucial element of any social technology, yet it is often the most vulnerable. The neurally connective nature of metaverse makes it very closely dependent on digital identity. While digital identity helps metaverse, by making it a user centric social technology, it also makes it quite vulnerable to identity hacking. Several crimes that occur in metaverse are interlinked with the theft of digital identity. The objective of this review paper is to conduct a comparative study on the currently existing methods that aim to preserve the digital identity of metaverse users. This comparative study will aim to understand each method based on two sets of parameters, which will give a clear report in terms of extent and availability of certain features that are necessary to prevent identity hacking. This study will also guide users in choosing the best suited preventive method for identity hacking, for their virtual environment. Finally, the outcome of this review is to discuss the functioning of each method and understand its role in preserving digital identity in metaverse and have a clear picture of the comparative study and choose the best method for this virtual environment.
  Keywords: metaverse; identity hacking; SSI; passwordless authentication; ZTA model; decentralised identity using blockchain; security; privacy.
  DOI: 10.1504/IJESDF.2026.10066334
   
  
• Behavioural cyber malware controller and blocker using block chain, machine learning, and VAPT  
  by Sulakshana B. Mane, Mohd Zafar Shaikh, Kiran Shrimant Kakade, Jayant Brahmane 
  Abstract: When talking about topics such as Digital India, the internet Era, or the internet of things, the role of providing people with a sense of safety is of the highest significance. Because we deal with such a huge number of different types of information, we are confronted with a wide variety of issues brought about by cyber infection. There is a kind of harmful software known as ransomware that may be discovered in internet. In addition, its influence will execute a variety of actions, including the theft of personal data, the misuse of data, and unauthorised access. When it spreads, it will encrypt your data and lock your machine. The purpose of this study is to explore the many different defensive techniques that might be used in response to ransomware attacks. In conclusion, the proposed algorithm got the best result in 66 ms, which was a classification accuracy of 99.41%.
  Keywords: digital India; cyber malware; ransom ware; security; block chain; preventative framework.
  DOI: 10.1504/IJESDF.2026.10066335
   
  
• Mortgage-based securities data hybrid encryption for financial data analysis  
  by Humashankar Vellathur Jaganathan  
  Abstract: Attribute-based encryption (ABE) is the most effective access control mechanism for protecting the cloud storage environment. The data of the data owner are separated into two distinct security levels and kept in various cloud providers according to our plan, which results in an increase in the degree of security that is provided by outsourcing data. Furthermore, our system, which is based on ciphertext-policy attribute-based encryption (CP-ABE), is able to not only offer a fine-grained access control for the data user, but it can also totally leverage the cloud side to permit outsourcing decryption. For the purpose of ensuring security, the FHE model achieved a gain of 88%, the AES approach achieved a gain of 81%, the RSA model reached 92%, and the PHE technique achieved 94%. A security level of 99.74% was obtained by the ALO-DHT model that was built. The ALO-DHT model that was built was successful in achieving 99% secrecy.
  Keywords: attribute-based encryption; multi-cloud; outsourcing decryption; access control.
  DOI: 10.1504/IJESDF.2026.10066461
   
  
• Big data security using homomorphic encryption: application in finance  
  by Tingxin Jiang  
  Abstract: Techniques that protect privacy make it possible to utilise private information without compromising the confidentiality of the information. The use of homomorphic encryption algorithms offers unique ways that make it possible to do computations on encrypted data while still preserving the secrecy of the information that is being protected. The use of homomorphic encryption methods is also discussed in relation to a security framework for Big Data analysis that is designed to protect individuals’ privacy. After that, we will proceed to provide a comparison of the properties that have been discovered in relation to the common homomorphic encryption tools that are now accessible. Analysis is performed on the outcomes of the installation of a variety of different homomorphic encryption toolkits, and a comparison is made between the various performances of each of these kits. The proposed model has an accuracy rate of about 93.75%.
  Keywords: big data; encryption algorithms; homomorphic encryption; privacy preserving; machine learning.
  DOI: 10.1504/IJESDF.2025.10066462
   
  
• Host-based threat hunting framework for log analysis  
  by Parag Shukla, Sandesh Ajgekar, Jay Teraiya 
  Abstract: Sysmon is a Windows system service and device driver. It is designed to persistently monitor and record system activity in the Windows event log. Sysmon tool is a data source for host-based intrusion detection and it is open-source and free. Being a sophisticated logging tool for Windows, Sysmon lacks suspicious activity identification, log parsing and analysis capabilities. We also need to check the reliability of Sysmon in intrusion detection as an endpoint visibility tool. Hence, as a supporting tool we developed the Huntmon framework for log parsing and to provide some basic capabilities that might be crucial in any type of investigation. This framework is a general multi-purpose Sysmon log parser tool. Along with the Sysmon log parsing, the huntmon framework provides dynamic analysis of Sysmon logs. This tool is compared with other traditional tools with the same test objects. The outputs of both tools are discussed in this paper.
  Keywords: system activity monitoring; host-based intrusion detection; log analysis; VirusTotal Lookup; portable executable headers; portable executable strings; process execution block; dynamic analysis.
  DOI: 10.1504/IJESDF.2026.10066475
   
  
• The importance of administrative enforcement in protecting the family from digital domestic violence, analytical study between reality and hope  
  by Jehad D. Aljazi, Odai Mohammad Ali Al Heilat 
  Abstract: This research addresses the legal issues related to the emergence of digital domestic violence, and the consequences of it, which led to the occurrence of crimes that are a great danger to society and the family. Therefore, administrative enforcement authorities intervene to prevent and limit their occurrence. This research also aims to clarify the position of Jordanian and Moroccan legislation sometimes in addressing digital domestic violence. The main problem of this paper revolves around the extent to which the Jordanian legislation achieves the objective of protecting the family from digital violence. To solve this problem, we divided this research into two parts. In the first, we dealt with administrative control measures in digital domestic violence, and in the second, administrative deterrent mechanisms in domestic violence. The research reached important outcomes; fore mostly is that the penalties imposed by the Jordanian legislator are insufficient to achieve effective protection from digital domestic violence. We recommend developing alternative penalties with significant effects aiming to correct the behaviour of the perpetrator of domestic violence.
  Keywords: administrative penalties; measures; family; behaviour; administrative control; digital violence; technical development; fine; Jordan.
  DOI: 10.1504/IJESDF.2026.10066524
   
  
• Encryption-based safe cloud data storage using Merkle hash sum tree with message authentication code  
  by G. Gangoni Vani , Y. Ambica, Rohita Yamaganti, Aruna Varanasi 
  Abstract: Security is the most important thing and seen mainly in computer applications, especially in the data of cloud computing security on storage plays an important role. The cloud provides a desirable platform for cognitive smart cities to access user data, enabling them to adapt their current actions and learn from past experiences. The data in a cloud has minimum security, because of mutable establishment procedure of data integrity. In this study, the Merkle hash sum tree with message authentication code (MHST-MAC) approach is proposed for enhancing the security in the cloud. This approach supports the privacy-preserving public auditing to give a secure storage to cloud. The files in a data are requested by a data owner (DO) that audits with third-party auditor (TPA) as well as multi-owner authentication approach is concerned when alternate processes to authenticate a user. The results show that a proposed MHST-MAC approach delivers the performance metrics such as encryption as well as decryption time values about the 126 ms and 323 ms for 200 (KB) file size compared with existing methods such as Rivest Shamir Adleman (RSA) and ElGamal algorithms.
  Keywords: cloud storage; decryption; encryption; message authentication code; Merkle hash sum tree; third-party auditor; TPA.
  DOI: 10.1504/IJESDF.2026.10066615
   
  
• Leveraging advanced deep learning algorithms to combat fake news in Arabic media landscape  
  by K. Chitra, E. Srimathi , R. Rajpriya, Edwin Shalom Soji, R. Balamurugan, S.Silvia Priscila 
  Abstract: The increasing presence of false information online in today’s digital era can lead to societal issues such as political upheavals and the circulation of incorrect data. This study introduces a unique method to identify untrue reports in Arabic utilising advanced deep learning methods. We review existing literature on fake news detection and discuss the challenges specific to Arabic language processing. Our approach entails creating a deep learning architecture customised to Arabic language and extensively evaluating its performance. Results, graphical representations, tables, mathematical equations, and model efficacy discussions are presented. While our approach shows promising results, we also acknowledge its limitations and propose future research directions. However, the journey continues. Research and innovation are needed to address dataset scarcity, model interpretability, and adversarial attacks. Staying ahead of disinformation providers’ increasingly sophisticated strategies is crucial as the digital world advances. This study guides us and reminds us of the challenges ahead. With this researchs tools and insights, we can fight fake news better.
  Keywords: false information identification; advanced learning methods; Arabic tongue; linguistic data processing; neural systems; text classification; sentiment analysis; information warfare.
  DOI: 10.1504/IJESDF.2026.10066981
   
  
• The impact of cybercrimes on the achievement of sustainable development goals. Analytical study  
  by Muaath S. Al-Mulla, May Hammoud, Ahmed Fekry Moussa, Jamal Barafi 
  Abstract: The study aims to highlight the impact of cybercrimes on achieving the goals of sustainable development. Artificial intelligence has changed the methods of these crimes and exacerbated the problem of the lack of sufficient international cooperation to reduce their effects, as well as the loss of efforts towards the idea of cybersecurity. We followed a descriptive-analytical approach that begins by explaining the development of cybercrimes, from the stage of human control to the stage of auto-control by machines, to highlight the weakness of the law and the faltering international efforts to confront these crimes and mitigate their effects. The study provides evidence that current national laws and international agreements are not effective in combating these crimes and reducing their risks to sustainable development. Recommendations include unifying international efforts to conclude an international agreement obligating the parties to confront these crimes.
  Keywords: cybercrime; artificial intelligence; sustainable development; law; international cooperation.
  DOI: 10.1504/IJESDF.2026.10067069
   
  
• Increasing network security using an enhanced hybrid deep intrusion detection model  
  by Jiacheng Wu, Tingting Jiang, Juan Li, Wujun Mei 
  Abstract: Internet of things (IoT) systems have recently seen a widespread use of machine learning (ML) methodologies for intrusion detection systems (IDSs). LSTM and GRU models, which are the RNNs, are used to identify the many kinds of threats that may occur in IoT systems. The Harris hawk optimisation and fractional derivative mutation methods are used in this study to perform feature choices. To evaluate the suggested technique, datasets that are accessible to the public were used. The empirical analysis revealed that the proposed method is superior to the other related approaches in accuracy and efficiency. The proposed model makes use of several databases. The proposed model attained a maximum accuracy of 100% in identifying attacks such as denial of service, exploits, generic, reconnaissance, and shellcode attacks respectively. This model provides a 99.7% accuracy rate.
  Keywords: intrusion detection systems; IDSs; machine learning; metaheuristics deep learning; long short-term memory.
  DOI: 10.1504/IJESDF.2026.10067347
   
  
• Securing renewable energy supply chains: challenges and opportunities for information security  
  by Al-Amin Abba Dabo, Amin Hosseinian-Far, Olalekan Adisa 
  Abstract: This paper explores the information security challenges and opportunities in renewable energy supply chains as they integrate advanced technologies from Industry 4.0 and Industry 5.0. Through a review of existing literature and industry insights, it identifies key sensitive data types at risk, such as intellectual property, operational data, and business strategies. The study highlights how the risk levels vary between Industry 4.0 and Industry 5.0 and examines the potential consequences of cyber-attacks, data breaches, and regulatory non-compliance. It also outlines opportunities to enhance security using advanced technologies, risk management strategies, and industry collaboration, offering valuable insights for stakeholders in the renewable energy sector.
  Keywords: renewable energy; supply chains; renewable supply chains; supply chain disruption; Industry 5.0; Industry 4.0; information security; cyber-attacks.
  DOI: 10.1504/IJESDF.2026.10067348
   
  
• Deep learning to detect social media fake news using sequence generative adversarial networks  
  by Saravanan Venkataraman, S. Albert Antony Raj, S. Belina V.J. Sara, S.Silvia Priscila 
  Abstract: Even if major efforts were made to verify the facts, the rising amount of fake news on social media, which has had a major influence on righteousness, confidence in others, and the community, persisted. This study offers SeqGAN to improve social media false news detection. By solving social media content’s unique problems, SeqGAN proves its sequence creation capabilities. The version uses SeqGAN’s generative energy to generate sensible text sequences and a discriminator network to identify bogus information narratives. Due to more accurate synthetic data, this unfavourable training method challenges the discriminator. The model’s ability to detect misinformation is tested here. It also evaluates SeqGAN on large datasets and uses multiple false news detection approaches to provide an overview. These findings illustrate the rise of bogus news, which could propagate on social media using SeqGAN. Experimental results reveal that the SeqGAN model outperforms standard false news detection approaches. The model is more sensitive to misinformation campaign linguistic and temporal quirks. The SeqGAN-based method works well in social media and online chat environments. Python’s model classifies instances with 98.5% accuracy and great proficiency, indicating its robustness in identifying truth.
  Keywords: sequence generative adversarial nets; SeqGAN; false news detection; social media; generative adversarial networks; misinformation campaigns.
  DOI: 10.1504/IJESDF.2026.10067349
   
  
• Cyberbullying detection and recognition using deep learning: SVM classification  
  by D. Maalini , I. Nandhini , S. Nelson , K. Umamaheswari  
  Abstract: Strong computational techniques are required for the growing importance of cyberbullying detection on social media platforms. A risk that some users may take use of these possibilities to humiliate, degrade, abuse, and harass other individuals. Research provides a comparative examination of various distinct deep learning procedures with the purpose of testing and evaluating the performance of deep learning methods in relation to a well-known worldwide Twitter dataset. The dataset in question is Twitter. Twitter is the dataset that you are referring to. The detection of abusive tweets and the discovery of remedies to the problems that are now being faced have both been accomplished via the use of attention-based deep learning algorithms. In order to extract the features, an application of the word2vec technique that was concatenated with CBOW was used. The proposed model achieves the accuracy rate of 90% for the given dataset.
  Keywords: cyberbully; RNN; CNN; LSTM; BiLSTM; word2vec; text classification.
  DOI: 10.1504/IJESDF.2026.10067637
   
  
• Unveiling Bitcoin transactions: a forensic framework beyond predefined artefacts  
  by Borase Bhushan Gulabrao, Digvijaysinh Rathod, Nitin Sharma, Aishwarya Tiwari 
  Abstract: This research presents a novel approach to Bitcoin forensics that transcends the limitations of predefined digital artefacts. This research delves into RAM and disk analysis associated with various Bitcoin wallet types. These wallets are studied on eight different parameters. The methodology includes structured and unstructured RAM analysis and disk analysis. This approach focuses beyond pre-defined artefacts and yields a comprehensive list of 33 digital artefacts linked to Bitcoin activities. Additionally, it identifies 46 crucial keywords that can empower LEAs to detect artefacts in seized digital devices. This research offers significant advancements in the field. The identified artefacts provide valuable insights into Bitcoin transactions, and the keywords serve as powerful tools for investigators. In conclusion, this study presents a groundbreaking framework for Bitcoin forensics. The study offers a more robust methodology for uncovering evidence of Bitcoin transactions. This research can significantly enhance investigative capabilities in the ever evolving realm of cryptocurrency crime.
  Keywords: Exodus; Electrum; ledger; OKX; public key; private key; passphrase; Trezor.
  DOI: 10.1504/IJESDF.2026.10067856
   
  
• The consequences of using autonomous weapon systems in light of international humanitarian law  
  by Naser Al Ali, Ziad Alwahshat, Vladimir Chebotarev 
  Abstract: Technological development, particularly in artificial intelligence, has led to the production of autonomous weapon systems. The use of this new type of weapon poses numerous challenges and issues regarding the suitability of the rules of international humanitarian law for these new means of warfare. This study aimed to explore the boundaries, prospects, and challenges of applying international humanitarian law to the use of autonomous weapon systems in armed conflicts, and to investigate their legitimacy. Therefore, the research aimed to address its primary inquiries, namely: what is the concept of autonomous weapon systems? What is the legitimacy of using autonomous weapon systems in armed conflicts? What is the legitimacy of using autonomous weapon systems in legitimate self-defence, and how can international humanitarian law be applied to the use of autonomous weapon systems in armed conflicts? What is the future of international engagement with autonomous weapon systems?
  Keywords: weapon systems; international humanitarian; killer robots; autonomous weapon; artificial intelligence.
  DOI: 10.1504/IJESDF.2026.10067920
   
  
• Digital technologies and legal regulation  
  by Nurlybek S. Nusipzhanov  
  Abstract: The purpose of the study was to clarify the need for a legal regulation of the digitalisation process in the field of civil relations. The methods that were used in the study are as follows: historical, statistical, analysis, induction, and comparative. The main results of the study should be highlighted as follows: analysis of the historical aspect of the development of digital technologies and their impact on the development of civil law; clarification of a number of specific terms, examination of their legislative interpretation; analysis of the state strategy of digital development of the Republic of Kazakhstan and highlighting positive aspects; analysis of the state of legislative regulation of the field and influence on the normal course of civil law relations in society, considering the risks of violation of authorship, confidentiality in the Republic of Kazakhstan and other countries; the positive world experience of regulating the relevant fields.
  Keywords: information technology; legislation; legal tech; digitisation; copyright issues; civil law.
  DOI: 10.1504/IJESDF.2026.10067921
   
  
• Blocking the internet and its impact on digital human rights and freedom  
  by Ruba Hmaidan, Tareq Al-Billeh, Ali Al-Hammouri, Abdulaziz Almamari, Mohammed Al Makhmari 
  Abstract: Many countries of the world suffer from the issue of blocking the internet. Meanwhile, some countries instituted protection against blocking the internet by considering that blocking the internet is an infringement of digital human rights that is enshrined in United Nations General Assembly resolutions. Blocking the internet can cause damage to health and education facilities and limit political participation. This blocking of the internet imposed by various governments in countries of the world causes serious harm to people’s human rights. Foremost among them is the right to freedom of expression and opinion. In that connection, it should be noted that the internet was blocked during election periods, protests, and the use of force by law enforcement agencies or the holding of examinations for some schools and universities. It should therefore be asked why the internet was blocked, which was usually accompanied by gross violations of digital human rights.
  Keywords: internet blocking; human rights; freedom of opinion; freedom of expression; digital rights; internet network.