International Journal of Information and Computer Security (11 papers in press)

Regular Issues

• Secure colour image encryption using improved ECC with KOAWOA algorithm and OTP generation  
  by Nilesh N. Thorat, Amit Singla, Tanaji Anandrao Dhaigude, Sumit Arun Hirve 
  Abstract: This research proposes a novel hybrid cryptographic framework that integrates improved elliptic curve cryptography (ImECC) with modified HMAC-based one-time password (MHOTP) generation, optimised through the newly introduced kookaburra merged walrus optimisation (KOAWOA) algorithm. The proposed method consists of two primary phases: embedding (encryption) and extraction (decryption). During the encryption process, three original images and two secret images undergo multi-layered encryption using random grid-based secret image sharing, extended visual cryptography scheme (EVCS), and kronecker product-based encryption, ensuring a highly secure and robust encryption mechanism. The KOAWOA algorithm is introduced as an advanced hybrid optimisation technique to generate optimal encryption keys, significantly enhancing security and reducing computational overhead. KOAWOA employs an adaptive metaheuristic strategy to improve randomness, making it highly resistant to attacks. Additionally, in the ImECC-based encryption stage, the MHOTP generation technique is utilised, where dynamically generated OTPs serve as encryption keys.
  Keywords: kookaburra merged walrus optimisation algorithm; improved elliptic curve cryptography; ImECC; visual cryptography; Baker’s map; MHOTP generation.
  DOI: 10.1504/IJICS.2026.10081171
   
  
• An efficient block cipher identification technique based on distance measurement and ensemble learning  
  by Xiaonian Wu, Bingyin Song, Runlian Zhang, Hailong Zhang 
  Abstract: Improving the accuracy of block cipher identification remains a significant challenge. To address this issue, we propose an efficient identification technique based on distance measurement and ensemble learning. First, plaintexts and their corresponding ciphertexts are generated and treated as vectors. The distance between each plaintextciphertext pair is then computed. These computed distances, together with the entropy of the ciphertexts, constitute a feature set. Using this feature set, the stacking technique is employed to construct an ensemble learning based classification model that integrates multiple machine learning algorithms, with five-fold cross-validation applied for both training and testing. Experimental results demonstrate that, compared with existing methods, the proposed model attains a highest five-class classification accuracy of 0.80 under the ECB mode and 0.61 under the CBC mode. Overall, this work contributes a hybrid feature set and a stacking ensemble model that achieve superior identification accuracy over existing approaches.
  Keywords: Block cipher identification; distance measurement; entropy; ensemble learning; stacking.
  DOI: 10.1504/IJICS.2026.10081479
   
  
• An interpretable ensemble learning approach with multi-feature behavioural intelligence for Lumma malware detection  
  by Romil Rawat, Shraddha Gupta, Swati Sharma, Shilpa Jaiswal, Kamal Kant Trivedi, T.M. Thiyagu 
  Abstract: To counter Lumma stealer malware threats bypassing traditional static and deep learning methods, this study proposes a Bayesian-optimised gradient boosting framework with in-fold selection (BAE-GBF). Evaluated on a balanced 2,000-sample dataset comprising 1,000 authentic Lumma variants and 1,000 benign executables across a 15-attribute feature space (PE headers, execution traces, network telemetry), the model applies strict in-fold synthetic Gaussian jitter and mutual information feature selection with correlation pruning (> 0.85) within an 80:20 stratified protocol (n = 400 holdout). BAE-GBF achieves 97.8% accuracy, a 97.2% F1-score, 98.1% precision, 96.3% recall, an AUC-ROC of 0.992, and a 2.9% false positive rate. It maintains a 4.2 ms inference time and 18.7 ms total processing time per sample. Additionally, it demonstrates high stability across ten random seeds (p < 0.01) and an 8.4% attack success rate under multi-domain perturbations.
  Keywords: Lumma malware; gradient boosting; behavioural analysis; network features; malware detection; adversarial robustness; cybersecurity analytics.
  DOI: 10.1504/IJICS.2026.10081537
   
  
• Malicious transaction detection in Bitcoin blockchain using ensemble learning approaches  
  by Abhiram Vaitla, Devidas S 
  Abstract: The growing adoption of cryptocurrencies has increased concerns about malicious activities on blockchain networks. Due to their pseudonymous nature and rapid transaction processing, identifying fraudulent Bitcoin transactions remains challenging. This study presents a practical and reproducible machine learning pipeline for detecting malicious Bitcoin transactions using the enriched Elliptic++ dataset. The proposed workflow involves data preprocessing, integration of transaction features with class labels, missing-value handling, and temporal data splitting to simulate real-world prediction scenarios. Pearson correlation analysis was conducted to assess the relevance of individual features to the target variable. Since the features exhibited weak correlations and none satisfied the predefined threshold for exclusion, all available features were retained for model training. We evaluated several machine learning models, including logistic regression, decision trees, random forests, XGBoost, SVM, and ensemble approaches. The results show that voting and stacking ensembles performed comparably to the best individual classifiers, highlighting their potential for blockchain security and financial forensics.
  Keywords: blockchain; financial forensics; ensemble learning; anomaly detection.
  DOI: 10.1504/IJICS.2026.10081588
   
  
• Security assessment of TWINE cipher under linked and statistical ineffective fault analysis  
  by Vahid Soleimani Hesari, Hadi Soleimany, Iman Mirzaali Mazandarani, Fatemeh Hossein-khani 
  Abstract: Linked ineffective fault analysis (LIFA) exploits deterministic relations between internal variables associated with ineffective faults, enabling key-dependent leakage even when faulty ciphertexts are unavailable. We investigate LIFA on TWINE, a lightweight block cipher based on a 16-branch generalised feistel network, and compare it with statistical ineffective fault analysis (SIFA). For single-link LIFA, one ineffective ciphertext reduces the candidate space for two four-bit last-round subkey nibbles from 28 to 28. We further introduce multiple linked ineffective fault analysis (MLIFA), which applies two linked S-box relations simultaneously and reduces the candidate space for four 4-bit last-round subkey nibbles from 216 to 28 per ineffective ciphertext. We validate both attacks through simulations and clock-glitch experiments on an ATmega328P microcontroller. The results show that LIFA and MLIFA require substantially fewer ineffective ciphertexts and provide greater robustness to fault-injection noise than SIFA.
  Keywords: generalised Feistel network; GFN; linked ineffective fault analysis; LIFA; lightweight block cipher; multiple linked ineffective fault analysis; MLIFA; statistical ineffective fault analysis; SIFA; TWINE.
  DOI: 10.1504/IJICS.2026.10081649
   
  
• A novel AI-based cloud healthcare security framework using optimised fire hawk XOR encryption and probabilistic super learning  
  by A.R. Arunarani, G. Sudhakaran, G. Logeswari, T.M. Thiyagu 
  Abstract: Modern society cannot do without healthcare, which demands secure data storage and transmission including prescriptions, medical history, and patient information. However, even though cloud computing offers flexible healthcare services; it raises serious privacy and security concerns. Thus, this research suggests a novel intelligent approach for healthcare data transmission using optimised fire hawks XOR (OFHXOR) and probabilistic super learning (PSL). In the proposed OFHXOR algorithm, the FHO is responsible for selecting the best encryption key for improving data security. Next, the PSL is used to detect attacks, select features, and change attack-related properties in order to boost learning performance. The suggested intelligent approach is helpful for real-time healthcare cloud computing, providing secure communication, efficient detection, robust access control, and computational efficiency. As can be seen from experiments, the OFHXOR-PSL model is superior to other techniques because it ensures 95.68% detection accuracy, 98.56% security level, and 4% attack rate.
  Keywords: cloud computing; fire hawk optimiser (FHO); probabilistic super learning; Matthews correlation coefficient.
  DOI: 10.1504/IJICS.2026.10081694
   
  
• Context-aware differential privacy for federated learning in healthcare  
  by Kalyani Pampattiwar, Namrata Patel, Masooda Modak 
  Abstract: Rising interest in collaborative medical artificial intelligence has encouraged adoption of federated learning to protect patient data. Yet many models add uniform differential privacy noise, ignoring patient age, disease severity and institutional risk, which weakens the trade-off between privacy and model utility in heterogeneous healthcare data. We present a context-aware differential privacy framework for federated learning that provides patient-level, context-specific privacy through five modules: patient-aware noise calibration network assigns noise from demographics and disease factors; disease-specific gradient clipping sets complexity-adjusted thresholds; federated context embedding exchange shares non-identifiable context embeddings; adaptive contextual utility estimator predicts local utility loss and adjusts noise; hierarchical privacy budget scheduler allocates budgets across patients, hospitals and disease cohorts. On multi-institutional healthcare datasets, the framework improves privacy efficiency by 25%, retains over 95% baseline accuracy and increases robustness under non-independent and identically distributed conditions, enabling practical, privacy-adaptive federated systems for clinical decision support at scale.
  Keywords: federated learning; differential privacy; healthcare AI; patient-specific privacy; context-aware privacy; process.
  DOI: 10.1504/IJICS.2026.10079981
   
  
• A fingerprint-based biometric cryptosystem for enhanced security and cryptographic key strength evaluation using statistical tests  
  by Reshma Nadaf, Satish S. Bhairannawar 
  Abstract: Confidential information transferred across unsecure channels can be securely protected cryptographic methods. In this paper, we propose a new encryption and decryption framework for the advanced encryption standards (AESs) algorithm, in which a user's fingerprint serves as the cryptographic key for safe communication. Our method extracts biometric features from the user's fingerprints using the minutiae extraction technique. By integrating encryption and extracted minutiae features into a probabilistic symmetric cryptosystem, our research suggests a way to minimise dependency on pseudo random number generators and reduce operational complexity. This research work proposes a novel key generation framework which uses key derivation function (KDF) with biometric features to generate a stable and unique 128-bit string used for encryption and decryption process. Statistical tests like frequency test, runs test, auto-correlation test and high density and low density tests are conducted to check randomness and unpredictability of cryptographic keys generated from fingerprint images.
  Keywords: biometrics; fingerprint; AES key generation; Biometric cryptosystem; statistical tests.
  DOI: 10.1504/IJICS.2026.10080165
   
  
• Graph anonymisation in online social networks using modified generative adversarial networks and variable Fossa optimisation algorithm  
  by Nithish Ranjan Gowda, Venkatesh, K.R. Venugopal 
  Abstract: In this paper, we propose a dual-method framework for OSN anonymisation that considers both privacy preservation and utility retention over graph data. In the first method, the deep learning model modified generative adversarial network (M-GAN) is used to successively acquire the privacy and utility objectives and build anonymised graphs. In that M-GAN-based graph anonymisation framework, the discriminator network is enhanced with an improved graph isomorphism network (I-GIN) integrated with the iPool algorithm, which retains the most informative features in arbitrary graphs. The second approach employs a variable Fossa optimisation algorithm (VFOSA), which significantly reduces structural information loss (SIL) during the clustering phase of the structural k-anonymity model, resulting in a highly efficient anonymised network. In VFOSA, the original Fossa optimisation algorithm is improved through a dynamic step-size mechanism. The performance of the proposed scheme is evaluated in terms of subgraph classification accuracy and node classification accuracy.
  Keywords: online social networks; OSNs; graph anonymisation; privacy preservation; modified GAN; improved graph isomorphism network; I-GIN; variable Fossa optimisation algorithm; VFOSA.
  DOI: 10.1504/IJICS.2026.10080166
   
  
• Shamir's sharing-based encryption: recursive sharing propagation for scalable and resilient symmetric encryption  
  by Ilhem Djeziri, Kamel Mohamed Faraoun 
  Abstract: This paper presents a recursive symmetric encryption framework based on Shamir-inspired polynomial transformations and evolving hidden states over 𝔾𝔽(2¹²⁸). Unlike CBC and CTR modes, which require one pseudorandom permutation (PRP) call per plaintext block, the proposed scheme propagates cryptographic randomness through recursive finite-field operations, reducing PRP usage. A hidden recursive state is initialised using a secure PRP, then plaintext blocks are recursively embedded into finite-field polynomials whose evaluations partially form the ciphertext while remaining shares are internally reused. This process produces algebraic diffusion and sequential ciphertext dependency. A formal analysis proves IND-CPA security under standard PRP assumptions and shows that the hidden states remain computationally indistinguishable from random values. Vandermonde-matrix optimisations enable scalable implementation with low overhead. Experimental results from a Rust implementation show improved performance over CBC and CTR while preserving strong avalanche and diffusion properties.
  Keywords: Shamir's secret sharing; provable symmetric encryption; scalable encryption; quantum-upgradeable security.
  DOI: 10.1504/IJICS.2026.10081084
   
  
• Efficient and secure heterogeneous signcryption for multi-message and multi-receiver communications in VANETs  
  by Mahesh Palakollu, Gowri Thumbur, P. Vasudeva Reddy 
  Abstract: Vehicular ad hoc networks (VANETs) enable direct communication among vehicles and roadside units to enhance traffic safety and management. However, the broadcast nature of wireless communication and the used of diverse cryptographic frameworks create serious security and interoperability challenges. To address these issues, this paper presents a pairing-free certificateless heterogeneous signcryption scheme supporting multi-messages and multi-receiver communication for VANETs. This scheme allows a sender in a certificateless cryptographic environment to securely transmit multiple messages to multiple receivers operating under public key infrastructure, without certificate management overhead or key escrow issues. Security is formally analysed in the ROM model under the hardness of the ECDLP. Performance analysis show that the scheme significantly improves the efficiency ranging from 49.23% to 96.65% over existing approaches. It also ensures confidentiality, authenticity, privacy preservation, and forward secrecy, making it suitable for resource-constrained VANET applications.
  Keywords: heterogeneous signcryption; certificateless framework; pairing-free; ECDLP; confidentiality and authentication; vehicular ad hoc networks; VANETs.
  DOI: 10.1504/IJICS.2026.10080475