International Journal of Information and Computer Security (8 papers in press)

Regular Issues

• IP obfuscation: a survey of methods and the introduction of transient key logic locking  
  by Arsalan Ali Malik, Neelam Nasir, Naveed Riaz, Naveed Ahmed, Mureed Hussain, Sajid Ali Khan, Ammar Masood 
  Abstract: Logic locking is a widely adopted hardware obfuscation technique which can be further sub-categorised into static and dynamic approaches based on the nature of the employed key. Besides being susceptible to SAT and fault injection attacks, static logic locking is vulnerable to widespread compromise from a single key exposure or device breach. On the other hand, dynamic logic locking introduces complexities in resource utilisation, key management, design, and adaptability. In this work, we provide a comprehensive and up-to-date overview of existing IP Obfuscation techniques, highlighting their strengths, and potential vulnerabilities. We also propose, a hybrid logic locking technique that capitalises on the positive attributes of both static and dynamic logic locking methodologies while minimising their inherent limitations. An initial proof-of-concept implementation/simulation has been performed on the Xilinx SP605 FPGA development board. The suggested transient key logic locking scheme is applicable to all type of IPs.
  Keywords: system-on-chip; SoC; field-programmable gate arrays; FPGAs; hardware security; IP protection; IP theft; obfuscation.
  DOI: 10.1504/IJICS.2025.10074686
   
  
• DMHDA: a model for real-time detection and mitigation of DDoS attacks in software defined networks  
  by Deepak Kumar, Jawahar Thakur 
  Abstract: Software-defined networks (SDN) offer significant flexibility, scalability, and dynamic management. However, these networks are increasingly vulnerable to distributed denial of service (DDoS) attacks. This study investigates the susceptibility of SDNs to such attacks and presents a DMHDA (real-time detection and mitigation of DDoS attacks). The model employs a unified capability for both detection and mitigation. It features a custom-developed script, ryu2m.js, for the real-time detection and mitigation, along with the elephant.py script to identify the route through which elephant flow occurs. The proposed model uses the sFlow tool to monitor the network traffic, and a virtual SDN environment consisting of virtual hosts, openvswitches, and a RYU controller. The sFlow-RT application provides visualisation of the topology used, the connection between switches, visualisation of traffic and topology. The findings emphasise its effectiveness in mitigating congestion caused by attacks, indicating a potential for significant improvements in security and performance within SDN environment.
  Keywords: distributed denial of service; DDoS; elephant flow; high-rate; intrusion detection systems; IDS; RYU; security; sFlow.
  DOI: 10.1504/IJICS.2026.10075570
   
  
• Deep learning driven fusion of iris biometrics for optimised multimodal authentication informative security  
  by S.V. Sheela, K.R. Radhika 
  Abstract: Secure authentication methods have been made possible by the high level of maturity obtained by biometric-based technologies. Artificial neural networks forecast non-parametrically using interconnected artificial neurons, like the biological nervous system. For verification, iris, hand geometry, handwriting, fingerprint, speech, retina, face, and typing rhythm were studied. Iris recognition is most popular because it accurately identifies people. This study uses iris biometric authentication. The method simulates CASIA-Thousand-Iris utilising deep convolutional neural network (DCNN) architectures EfficientNetB0.1, CNN, DenseNet, and ConvNeXt. The experiment used our retinal recognition method to accurately identify numerous retinal samples. The suggested study introduced an MSAGFF module to EfficientNetB0.1 for iris biometrics. The attention mechanism uses channel spatial attention (CSA) to reduce redundant information and improve discriminative features for accurate recognition. Adaptive fusion strategy dynamically integrates recovered features from different receptive fields to increase model durability and decision-making. For secure Iris-based identification, EfficientNetB0.1s multimodal authentication is reliable. This end-to-end strategy boosts system performance. CNN (98.79%), DenseNet (92.11%), and ConvNeXt (66.66%) had worse accuracy than EfficientNetB0.1 (99.33%). CNN architectures in biometric systems are extended by deep learning-based iris recognition for safe authentication.
  Keywords: retina; convolutional neural network; CNN; informative security; multi-factor authentication; biometric identification; EfficientNetB0.1; DenseNet; ConvNeXt.
  DOI: 10.1504/IJICS.2026.10075572
   
  
• Preventing playback attacks in fingerprint biometrics through four-level hashing  
  by H. Mohamed Khan 
  Abstract: Replay (playback) attacks threaten fingerprint biometric systems by re-using previously captured data to gain unauthorised access. This paper proposes fingerprint matching using four-level hashing method (FMFLHM) - a layered approach combining timestamp transformation, fingerprint trait modification, XOR mixing, triangular hashing (three iterative hashes), and AES encryption. FMFLHM fuses ridge orientation and Gabor-HOG descriptors, binds them to time and a server nonce, and transmits an AES-encrypted triangular hash for server verification. Experiments on NIST-302 and MOLFDB datasets show FMFLHM reduces false acceptance and rejection rates and gives higher authentication accuracy (98.09% on NIST-302). We discuss key-management, scalability for resource-constrained devices, and limitations versus advanced spoofing techniques. FMFLHM strengthens resistance to replay attacks and offers a practical route to more robust fingerprint authentication.
  Keywords: NIST; playback attack; hashing; cybersecurity; ridge orientation; histogram of gradients.
  DOI: 10.1504/IJICS.2025.10074825
   
  
• MIMO-cooperative-spectrum sensing with trust and reputation method in CRN on blockchain using Adam-gradient descent Archimedes algorithm  
  by Sandip Babanrao Shrote, Sadhana D. Poshattiwar 
  Abstract: Cognitive radio networks (CRNs) have emerged as a promising solution to address spectrum scarcity. A fundamental component of CRNs is spectrum sensing (SS), as it identifies spectrum holes accurately and reliably. However, efficient and accurate SS remains a major challenge. Here, Adam-gradient descent Archimedes algorithm (Adam-GDAA) is designed for multiple-input multiple-output (MIMO)-CSS in CRN. Firstly, the MIMO-CSS system model is simulated. Thereafter, SS is conducted based upon the Renyi-entropy weighted Adam-GD algorithm. However, weight determination is done by employing the Adam-GD algorithm. Thereafter, output 1 is obtained, and then, the data is recorded in BC. After that, trust and reputation-based SS is accomplished, and therefore, output-2 is obtained. Lastly, both outputs are merged, and weights are generated utilising Adam-GDAA. The Adam-GDAA obtained a computation time of 0.829 sec, sensing time of 222.110 sec, and probability of false alarm of 0.330, and probability of detection of 0.913.
  Keywords: cognitive radio network; CRN; spectrum sensing; SS; multiple-input and multiple-output; MIMO; blockchain; BC; trust and reputation.
  DOI: 10.1504/IJICS.2025.10074618
   
  
• ReBIoT: a resource-efficient framework for blockchain-enabled IoT and distributed large-scale applications  
  by Muhammad Imran, Bin Yao 
  Abstract: Blockchain-enabled internet of things (BIoT) has emerged as a promising paradigm to enhance security and privacy in distributed environments. While blockchain ensures data integrity through immutable and tamper-proof records, its adoption in resource-constrained devices (RCDs) remains challenging due to computationally intensive consensus mechanisms and inefficient storage practices. This paper introduces ReBIoT, a secure and lightweight framework designed to optimise blockchain integration within RCD-based systems. ReBIoT decentralises intelligence by enabling interactive decision-making among nodes, replacing traditional heavy consensus verification. It further incorporates a self-learning rule-based data filtering (SRDF) mechanism to remove redundant or irrelevant data, and applies forking and versioning with fixed-length chunking to improve immutability and storage efficiency. The layered architecture of ReBIoT progressively filters raw data and prevents duplication, providing a flexible interface for various distributed applications. Experimental evaluations demonstrate that ReBIoT significantly reduces computational and storage overheads while maintaining data reliability and security.
  Keywords: blockchain; internet of things; IoT; consensus algorithm; energy efficiency; filtering; forking; versioning.
  DOI: 10.1504/IJICS.2026.10075667
   
  
• Generalised multi-secret sharing scheme for non-monotone access structures  
  by Shiva Krishna Nallabothu, N. Rukma Rekha, Y.V. Subba Rao 
  Abstract: Generalised secret sharing (GSS) schemes for monotone access structures, which always grant access to larger sets, offer flexibility beyond threshold schemes. But often these schemes struggle with practical limitations and an inability to model complex real-world policies involving specific exclusions or conditional access based on the absence of certain participants. We propose a novel GSS scheme specifically designed for non-monotone access structures, enabling true fine-grained control for scenarios where exceptions or negative constraints are paramount. The proposed GSS scheme is then extended to a generalised multi-secret sharing (GMS) scheme, facilitating the secure and controlled distribution of multiple secrets under the non-monotone access structure. GSS and GMS schemes are constructed using maximum distance separable (MDS) codes and require one-way functions. The proposed schemes are ideal and computationally perfect. The correctness, complexity, and security analysis are also given.
  Keywords: generalised secret sharing; non-monotone access structure; MDS codes.
  DOI: 10.1504/IJICS.2025.10075007
   
  
• MTD-integrated ABAC: integrating moving target defence into attribute-based access control for insider threat mitigation  
  by Olusesi Balogun, Mohammad GhasemiGol, Zhipeng Cai, Daniel Takabi 
  Abstract: Insider threats are prevalent security issues for organisations. While attribute-based access control (ABAC) systems manage sensitive data, they are not fully effective against insider threats. We propose integrating moving target defence (MTD) into ABAC systems to mitigate these threats. Our approach enhances the ABAC system with three modules: 1) a correlated attribute generator to estimate correlations among attribute-value pairs; 2) a policy sensitivity estimator to determine sensitivity levels of policy rules; 3) a mutation engine to dynamically mutate sensitive policy rules using correlated attributes. We evaluated our framework using a real-world dataset from an educational system, assessing the efficiency of the attribute generator, efficiency of the sensitivity estimator, overhead from the MTD components, and the framework's overall performance. Our results show that with a dataset of 200,000 records and 13 policy rules, the framework identified five sensitive rules and achieved a 100% mitigation rate without excessive overhead.
  Keywords: insider threat; moving target defence; MTD; attribute-based access control; ABAC; correlated attributes; policy sensitivity; policy mutation.
  DOI: 10.1504/IJICS.2026.10075568