Forthcoming and Online First Articles

International Journal of Critical Computer-Based Systems

International Journal of Critical Computer-Based Systems (IJCCBS)

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International Journal of Critical Computer-Based Systems (4 papers in press)

Regular Issues

  • An Enhanced Digital Image Watermarking Technique using DWT-HD-SVD and Deep Convolutional Neural Network   Order a copy of this article
    by Manish Rai, Sachin Goyal, Mahesh Pawar 
    Abstract: This paper proposes a novel image watermarking model, which combines discrete wavelet transform (DWT), Hessenberg decomposition (HD), singular value decomposition (SVD)-based deep convolutional neural networks (D-CNN) technique to explore the subjective and objective quality of the images. Initially, the source and cover image are preprocessed using random sampling techniques. During the process of embedding a watermark image, the cover image is decomposed into a number of sub-bands using the DWT process and the resulting coefficients are fed into the HD process. In continuation to it, the source image is operated on the SVD simultaneously and finally, the cover image is embedded into the source image by the attack-defending process. The probability of data loss during the watermarking extraction process and this issue is postulated by the D-CNN technique that explores the denoising process on the extracted watermarked images. The experimental results show that the proposed method has a good trade-off between robustness and invisibility even for the watermarks with multiple sizes.
    Keywords: watermarking; discrete wavelet transform; DWT; singular value decomposition; SVD; deep convolutional neural networks; D-CNN; watermarking embedding; extraction process.
    DOI: 10.1504/IJCCBS.2022.10050956
  • Task Models for Mixed Criticality Systems - A Review   Order a copy of this article
    by Louella Colaco, Arun S. Nair, Biju Raveendran, Sasikumar Punnekkat 
    Abstract: The past decade has seen tremendous interest in mixed criticality systems research due to its exponential growth with inherent challenges of effective resource utilisation and isolation. The pervasiveness of these systems along with their certification needs, prompt for suitable task models to perform the required analysis. Extensive usage scenarios and strict certification requirements have spawned a broad spectrum of research and evolved into several task models. In this work, a thematic survey of task models for both uni-core and multi-core mixed criticality systems is carried out. The work categorises task models based on attributes such as resources, quality of service, operating system overheads, energy, fault tolerance and parallel processing. After synthesising the state-of-the-art, the work summarises task models by providing a visual aid and a ready reckoner with traceability to mixed criticality challenges. This work serves as a quintessential reference manual for researchers and academicians in the mixed criticality domain.
    Keywords: uni-core mixed criticality systems; multi-core mixed criticality systems; task models for mixed criticality systems.
    DOI: 10.1504/IJCCBS.2022.10053341
  • Detection of cyber-attacks for sensor measurement data using supervised machine learning models for modern power grid system   Order a copy of this article
    by Manikant Panthi, Tanmoy Kanti Das 
    Abstract: The smart power grid systems are continually exposed to malicious cyber-attacks that are difficult to detect. If smart power grid attacks are not identified quickly and correctly, they may cause substantial economic losses and damage to the power system. To enhance productivity and improve the security of the smart power grid system against cyber-attacks, real-time detection of smart power grid attacks is still challenging. In recent years, there have been more cyberattacks, which have caused a lot of damage to power systems. This paper presents an experimental investigation of seven different approaches for detecting malicious activities and cyberattacks in the smart power grid system. Further, we employed maximum relevancy and minimum redundancy-hesitant fuzzy set feature selection technique to boost the attack detection performance. The experimental results demonstrate that random forest achieved the highest performance and average accuracy for two-class (95.30%) and three-class (95.33%) classifications, which shows that the presented proposed Model notably outperformed the other cyber-attack detection models.
    Keywords: SCADA; MRMR-HFS; cyber-attacks; machine learning.
    DOI: 10.1504/IJCCBS.2022.10054895
  • A Deterministic Approach with Markov Chain Algorithm to Impose Higher Order Security in Dynamic Wireless Sensor Networks (DWSN)   Order a copy of this article
    by M. S. S. Sasikumar, Narayanan A.E 
    Abstract: Wireless sensor networks are deployed based on their application environment. Since WSN are modality based, they sense data based on the type of data, they must sense. Unless otherwise we deploy proper algorithms and governance on to the sensor nodes, they behave in a normal way. We tried an attempt to provide a pilot scheme to establish a scheme called ORDER optimal retrospective decision enrichment for reliability, which analyses the previous tasks of the sensor nodes and categorise those nodes under different categories related to specific tasks. The algorithm ORDER analyses the tasks and assigns the tasks to the sensor nodes and monitor whether the tasks are being performed in a time bound manner without any compromise into security aspects. Our model is compared with the existing benchmarked approaches and found better in terms of reliability, data integrity and data consistency, throughput and BER.
    Keywords: ORDER; Markov chain; deterministic; retrospective; sensor nodes; reliability; FEDSEC; adversarial; security; federated learning.
    DOI: 10.1504/IJCCBS.2024.10058153