Forthcoming Articles

International Journal of Information and Computer Security

International Journal of Information and Computer Security (IJICS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Information and Computer Security (2 papers in press)

Regular Issues

  • Deep learning driven fusion of iris biometrics for optimised multimodal authentication informative security   Order a copy of this article
    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
     
  • Optimised compact authentication scheme based on three factors for cloud-based electronic transactions   Order a copy of this article
    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
     

 

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