International Journal of Information and Computer Security (9 papers in press)

Regular Issues

• Optimised compact authentication scheme based on three factors for cloud-based electronic transactions  
  by Renuka Kondabala, Savadam Balaji, S. Sai Anuraghav 
  Abstract: Cloud services provide seamless data sharing, storage, and processing, enabling for the development of scalable applications and services capable of responding to real-time events. But as cloud technology becomes more common in daily life, it presents serious security risks, especially in relation to data breaches, illegal access, and complying with regulations standards. In order to increase the security of cloud-based electronic transactions, this study proposes a novel multi-factor authentication framework. In order to detect intrusions and reject malicious data before it is stored, the proposed approach incorporates an adaptive neuro-fuzzy inference system (ANFIS). Furthermore, sensitive user data, such as credentials and biometric information, is protected with homomorphic encryption (HE) for enhanced privacy. The framework for security is constructed with an optimised compact authentication (OCA) scheme that consists of three phases: setup, registration, and authentication. The system solves important vulnerabilities such session key leakage and provides procedures for user revocation and re-registration. The model performs significantly better than existing security methods when evaluated using throughput, latency and packet loss ratio. The Python platform is used to develop and test the complete system, proving its efficacy in boosting trust among users in cloud services and protecting electronic transactions conducted in the cloud.
  Keywords: cloud-based electronic transactions; ANFIS; optimised compact authentication; OCA; homomorphic encryption; HE.
  DOI: 10.1504/IJICS.2026.10076727
   
  
• Quantum-enhanced autonomous DNS over HTTPS security (CITADEL-DoH)  
  by Basharat Ali, Guihai Chen 
  Abstract: The security of DNS over HTTPS (DoH) faces escalating challenges driven by advances in quantum computing, adversarial artificial intelligence, and increasingly sophisticated attack strategies targeting encrypted communication channels. While existing protection mechanisms remain effective against known threats, they exhibit structural limitations when confronted with adaptive, multi-layered, and post-quantum adversarial models. To address these deficiencies, this study introduces CITADEL-DoH, a unified security architecture integrating quantum-resistant AI through hybrid LWE-CKKS encryption, decentralised trust enforcement via a proof-of-trust (PoT) protocol combined with federated byzantine agreement (FBA), and hardware-assisted integrity verification using FPGA-accelerated verifiable delay functions (VDFs). Novel contributions further include the application of topological data analysis (TDA) for encrypted anomaly detection and liquid time-constant (LTC) networks for adaptive traffic modelling. Experimental evaluation demonstrates 99.3% detection accuracy for domain generation algorithm (DGA) attacks and sub-millisecond query verification latency. Results confirm enhanced scalability, resilience to evasion, and robustness against emerging quantum-era threats.
  Keywords: network protocols; network security; DNS over HTTPS; enhancing network security; attack detections; AI and network security; cyber attacks in networks.
  DOI: 10.1504/IJICS.2026.10077135
   
  
• Hybrid energy and opcode sequence-based detection of automation attacks in online social networks using SSFCM classification  
  by Anjali Rawat, Anand Rajavat 
  Abstract: Online social network automation attacks (OSNAA) increasingly employ automated tools to perform malicious activities such as bot-based interactions, email hijacking, and malware-driven manipulation. This study introduces an automated social network attack detection model (ASNADM) that integrates energy consumption footprint analysis (EComp-FP) with automated software opcode sequence analysis (ASOSA-OSM) to identify automation-based anomalies at the client side. The framework analyses behavioral energy traces from CPU and system activity alongside opcode n-gram representations derived from executable binaries using opcode frequency variance (OFV) and weighted term frequency (TF-W). These heterogeneous features are fused and classified using self-adaptive soft fuzzy C-means (SSFCM) clustering. Experiments conducted on the SPEMC-15K-E dataset demonstrate a detection accuracy of 99.93% with a 0.07% false-positive rate, outperforming DT, KNN, RF, and SVM models. Results confirm that abnormal energy patterns and low-entropy opcode sequences effectively reveal malicious automation in online social networks.
  Keywords: online social network automation attacks; OSNAA; energy consumption footprint analysis; EComp-FP; opcode sequence mining; self-adaptive soft fuzzy C-means; SSFCM; client-side anomaly detection.
  DOI: 10.1504/IJICS.2026.10077393
   
  
• DuoNet: a hybrid deep learning model for shilling attack detection in recommendation systems  
  by M. Sunitha, Naramula Venkatesh 
  Abstract: The study proposes a hybrid deep learning model, DuoNet, designed to detect and mitigate shilling attacks effectively. Data is collected from social media networks and e-commerce platforms, capturing user-item rating interactions. The pre-processing stage involves removing duplicate entries, imputing missing values using mean imputation and scaling the data with the min-max normalisation technique to ensure consistency. DuoNet integrates two advanced methodologies: T-Bi-LSTM for extracting temporal features and OCNN for capturing spatial features. The improved seagull optimisation algorithm (ISOA) optimises the CNNs hyperparameters, enhancing the models overall performance. The classification layer in the CNN combines temporal and spatial features to predict whether a user profile is genuine or represents a shilling attack. Experimental evaluations conducted on datasets from Amazon and Netflix demonstrate that DuoNet outperforms existing models, achieving higher accuracy, precision, F1-score, recall, and specificity.
  Keywords: shilling attacks; DuoNet; T-Bi-LSTM; OCNN; improved seagull optimisation algorithm; ISOA; temporal features and spatial features.
  DOI: 10.1504/IJICS.2026.10077481
   
  
• SISS-FSSI: secret image sharing scheme with flexible sized shadow images  
  by Vamsidhar Kolukuluri, B.R. Purushothama 
  Abstract: Secret image sharing allows a secret image to be divided among multiple users, enabling reconstruction by collecting a predetermined number of images. One key challenge is minimising the size of these shadow images. This study introduces a new technique that employs downsampling and a public difference image to address this issue. By using average pooling for downsampling, we reduce the hidden images size while preserving essential visual details. A public difference image is created by calculating the difference between the downsampled and permuted image, enabling recovery of the original image even if many shadow images are lost. Our method allows for adjustable pooling sizes, which control downsampling and maintain vital visual information. This reduction in size enhances the efficiency of the image-sharing system, making it more suitable for transmission and storage. Extensive evaluations across various scenarios confirm the approachs effectiveness, flexibility, and advantages over existing methods for secure image transmission and storage.
  Keywords: secret image sharing; public image; shadow images; lagrange interpolation; pooling; lossless reconstruction.
  DOI: 10.1504/IJICS.2026.10077599
   
  
• Detecting and countering battery-depletion attacks in wearable medical devices  
  by Shakir A. Mehdiyev 
  Abstract: Although large-scale cyberattacks on wearable medical devices (WMDs) are not yet widespread, the growing complexity and connectivity of these systems make them a potential target for battery-draining threats. Devices such as pacemakers, insulin pumps, and neurostimulators are critically dependent on a stable power supply to perform life-sustaining functions. This article explores the possible causes of increased energy consumption, including cyber impacts on sensor, computational, and communication modules. In response to these challenges, the paper proposes an approach that combines adaptive power management with a lightweight intrusion detection system tailored to the specific characteristics of WMDs. The study emphasises the importance of implementing energy-efficient and proactive strategies to enhance the resilience of these devices against emerging cyber-physical threats.
  Keywords: wearable medical devices; WMDs; sensors; energy vulnerabilities; battery failures; cyberattacks; energy monitoring.
  DOI: 10.1504/IJICS.2026.10077999
   
  
• Context-aware sensitive information detection in unstructured text using BERT  
  by Longjam Velentina Devi, Navanath Saharia 
  Abstract: This paper proposes a BERT-based token classification model specifically designed for detecting sensitive information inside unstructured text data. Unlike existing sentence level or context level classification methods, our methodology allows for finer granularity by examining individual words as well as their impact on one another. Previously available methods often misclassify non-sensitive information such as customer care or toll-free numbers as sensitive data, but our methodology effectively distinguish between genuinely sensitive information and non-sensitive public contact numbers, improving precision and reducing false positives. Our model surpasses previous techniques on key criteria, with an accuracy of 98%, precision of 0.98 and F1-score of 0.99 performing better than the existing model by a considerable margin. This study highlights the effectiveness of classification for sensitive data detection and establishes a new benchmark for token-level analysis contributing to more secure and effective sensitive content management.
  Keywords: bidirectional encoder representations from transformers; BERT; privacy; machine learning; security; privacy; deep learning; CRF; sensitive information; personally identifiable information; PII.
  DOI: 10.1504/IJICS.2026.10078195
   
  
• Fake profile detection on social media using hybrid 2D CNN and AES-BiLSTM with network analysis  
  by Sujit Kumar Badodia, Hemant Makwana 
  Abstract: Detecting and eliminating fake profiles is crucial to maintaining the integrity of social media platforms and ensuring user safety. This paper proposed a hybrid artificial intelligence model to address these challenges by combining advanced preprocessing, feature extraction and classification techniques. For feature extraction, principal component analysis (PCA) is used to decrease the dimensionality of the data, ensuring efficient processing. The hybrid classification framework integrates a 2D convolutional neural network (2D CNN) to extract spatial features from the input data. Additionally, an attention-enhanced stacked BiLSTM (AES-BiLSTM) is utilised to capture temporal features, while the added attention mechanisms improve the models focus on the most relevant information. The 2D CNN and AES-BiLSTM models are further enhanced through hybrid optimisation, with their hyperparameters fine-tuned using the seagull optimisation algorithm (SOA). This approach achieves a high accuracy of 98.9%, precision of 98.9%, specificity of 98.9%, and an F1-score of 99%.
  Keywords: principal component analysis; PCA; 2D convolutional neural network; 2D CNN; AES-BiLSTM; fake profile detection; seagull optimisation algorithm; SOA.
  DOI: 10.1504/IJICS.2026.10078196
   
  
• Security overview and performance assessment of fully homomorphic encryption in machine learning  
  by Davor Vinko, Kruno Miličević, Ivan Uglik, Adrijan Đurin, Madhurima Ray, Richard Lomotey 
  Abstract: In the era of cloud computing and third-party data processing, protecting data during computation remains a major challenge, as traditional encryption secures data only at rest and in transit. Fully homomorphic encryption (FHE) addresses this limitation by enabling computation directly on encrypted data. This paper evaluates the performance of FHE for machine learning using concrete ML, a library designed for FHE-based models. Ten machine learning and deep learning algorithms were implemented using both scikit-learn and concrete ML to compare training time, execution time, and accuracy. While FHE enables execution on encrypted data, only one of the tested algorithms supports training on encrypted data; the remaining models require plaintext training. Results indicate a significant computational overhead for FHE-based models, whereas accuracy remained comparable, with deviations typically below 1% and never exceeding 5%. Despite current performance limitations, FHE offers inherent resistance to quantum attacks and strong potential for privacy-preserving machine learning.
  Keywords: security; fully homomorphic encryption; FHE; machine learning; performance assessment; data protection; data encryption; data processing; encryption methods; deep learning.
  DOI: 10.1504/IJICS.2026.10078235