Most recent issue published online in the International Journal of Critical Computer-Based Systems.
International Journal of Critical Computer-Based Systems
http://www.inderscience.com/browse/index.php?journalID=325&year=2023&vol=10&issue=4
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International Journal of Critical Computer-Based Systems
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International Journal of Critical Computer-Based Systems
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http://www.inderscience.com/browse/index.php?journalID=325&year=2023&vol=10&issue=4
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An enhanced digital image watermarking technique using DWT-HD-SVD and deep convolutional neural network
http://www.inderscience.com/link.php?id=136317
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.
An enhanced digital image watermarking technique using DWT-HD-SVD and deep convolutional neural network
Manish Rai; Sachin Goyal; Mahesh Pawar
International Journal of Critical Computer-Based Systems, Vol. 10, No. 4 (2023) pp. 269 - 286
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.]]>
10.1504/IJCCBS.2023.136317
International Journal of Critical Computer-Based Systems, Vol. 10, No. 4 (2023) pp. 269 - 286
Manish Rai
Sachin Goyal
Mahesh Pawar
Department of CSE, RGPV University, Bhopal, MP 462023, India ' Department of IT, RGPV University, Bhopal, MP 462023, India ' Department of IT, RGPV University, Bhopal, MP 462023, India
watermarking
discrete wavelet transform
DWT
singular value decomposition
SVD
deep convolutional neural networks
D-CNN
watermarking embedding
extraction process
2024-01-30T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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4
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286
2024-01-30T23:20:50-05:00
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Task models for mixed criticality systems - a review
http://www.inderscience.com/link.php?id=136319
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.
Task models for mixed criticality systems - a review
Louella Colaco; Arun S. Nair; Biju K. Raveendran; Sasikumar Punnekkat
International Journal of Critical Computer-Based Systems, Vol. 10, No. 4 (2023) pp. 287 - 329
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.]]>
10.1504/IJCCBS.2023.136319
International Journal of Critical Computer-Based Systems, Vol. 10, No. 4 (2023) pp. 287 - 329
Louella Colaco
Arun S. Nair
Biju K. Raveendran
Sasikumar Punnekkat
Department of Computer Science and Information Systems, BITS Pilani K.K. Birla Goa Campus, Sancoale, Goa, India ' Department of Computer Science and Information Systems, BITS Pilani K.K. Birla Goa Campus, Sancoale, Goa, India ' Department of Computer Science and Information Systems, BITS Pilani K.K. Birla Goa Campus, Sancoale, Goa, India ' Dependable Software Engineering, Mälardalen University, Västerås, Sweden
uni-core mixed criticality systems
multi-core mixed criticality systems
task models for mixed criticality systems
2024-01-30T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
10
4
287
329
2024-01-30T23:20:50-05:00
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Detection of cyber-attacks for sensor measurement data using supervised machine learning models for modern power grid system
http://www.inderscience.com/link.php?id=136320
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.
Detection of cyber-attacks for sensor measurement data using supervised machine learning models for modern power grid system
Manikant Panthi; Tanmoy Kanti Das
International Journal of Critical Computer-Based Systems, Vol. 10, No. 4 (2023) pp. 330 - 354
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.]]>
10.1504/IJCCBS.2023.136320
International Journal of Critical Computer-Based Systems, Vol. 10, No. 4 (2023) pp. 330 - 354
Manikant Panthi
Tanmoy Kanti Das
Department of Computer Application, National Institute of Technology Raipur, Chhattisgarh, 492010, India ' Department of Computer Application, National Institute of Technology Raipur, Chhattisgarh, 492010, India
SCADA
MRMR-HFS
cyber-attacks
machine learning
2024-01-30T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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330
354
2024-01-30T23:20:50-05:00
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Tuna swarm optimisation-based feature selection and deep multimodal-sequential-hierarchical progressive network for network intrusion detection approach
http://www.inderscience.com/link.php?id=136338
Network intrusion detection system (NIDS) is important for securing network information. Neural network (NN) has recently been used for NIDS, which gained prominence results. Conventional neural network (CNN) has been introduced in network traffic data because of its single structure. The classification of assaults will no longer be useful due to redundant or inefficient features. Tuna swarm optimisation (TSO) has been introduced for feature selection (FS). First, pre-processing and feature extraction stages enable more efficient processing of features if handled independently. In order to examine the exploration space accuracy and position the best features, the second feature selection step of the TSO methodology involved selecting a subset of features by reducing the number of features. Lastly, multimodal deep auto encoder (MDAE) and gated recurrent unit (GRU) allow deep multimodal-sequential-hierarchical progressive network (DMS-HPN) intrusion detection method. Its DMS-HPN technique would routinely learn the temporal features among neighbouring network connections, simultaneously integrating diverse feature information inside a network. Datasets like UNSW-NB15 and CICIDS 2017 assess the effectiveness of the proposed DMS-HPN approach. Classification algorithms are evaluated via precision, recall, F-measure, and accuracy. Compared to conventional classifiers, the presented DMS-HPN classifier achieves the greatest accuracy.
Tuna swarm optimisation-based feature selection and deep multimodal-sequential-hierarchical progressive network for network intrusion detection approach
G. Gowthami; S. Silvia Priscila
International Journal of Critical Computer-Based Systems, Vol. 10, No. 4 (2023) pp. 355 - 374
Network intrusion detection system (NIDS) is important for securing network information. Neural network (NN) has recently been used for NIDS, which gained prominence results. Conventional neural network (CNN) has been introduced in network traffic data because of its single structure. The classification of assaults will no longer be useful due to redundant or inefficient features. Tuna swarm optimisation (TSO) has been introduced for feature selection (FS). First, pre-processing and feature extraction stages enable more efficient processing of features if handled independently. In order to examine the exploration space accuracy and position the best features, the second feature selection step of the TSO methodology involved selecting a subset of features by reducing the number of features. Lastly, multimodal deep auto encoder (MDAE) and gated recurrent unit (GRU) allow deep multimodal-sequential-hierarchical progressive network (DMS-HPN) intrusion detection method. Its DMS-HPN technique would routinely learn the temporal features among neighbouring network connections, simultaneously integrating diverse feature information inside a network. Datasets like UNSW-NB15 and CICIDS 2017 assess the effectiveness of the proposed DMS-HPN approach. Classification algorithms are evaluated via precision, recall, F-measure, and accuracy. Compared to conventional classifiers, the presented DMS-HPN classifier achieves the greatest accuracy.]]>
10.1504/IJCCBS.2023.136338
International Journal of Critical Computer-Based Systems, Vol. 10, No. 4 (2023) pp. 355 - 374
G. Gowthami
S. Silvia Priscila
Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India ' Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India
network intrusion detection systems
NIDS
feature selection
FS
multimodal deep auto encoder
MDAE
conventional neural network
CNN
gated recurrent unit
GRU
tuna swarm optimisation
TSO
2024-01-30T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
10
4
355
374
2024-01-30T23:20:50-05:00