Optimal feature selection for malware detection in cyber physical systems using graph convolutional network

Daniel, A.; Deebalakshmi, R.; Thilagavathy, R.; Kohilakanagalakshmi, T.; Janakiraman, S.; Balamurugan, B.

doi:10.1016/j.compeleceng.2023.108689

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…We have only included studies (including technical articles and excluding review-type articles) focused on both areas. Table 7 summarizes the methodologies and results of the applications [115][116][117][118][119][120][121][122][123][124][125][126][127].…”

Section: Recent Cps and Iot Applicationsmentioning

confidence: 99%

“…To ensure an up-to-date discussion, only works published in 2023 were included in Table 7. In general, existing works have tackled CPS and IoT applications using classification [115,[118][119][120][121][122][123][124]127], regression [125], and deep learning [118][119][120][121][122]126] approaches. More investigations can be conducted using clustering, transfer learning, and data generation algorithms.…”

Section: Recent Cps and Iot Applicationsmentioning

confidence: 99%

“…Bidirectional long short-term memory network with an attention mechanism Root-mean-square error of 0.011 and a mean average error of 0.002 [120] Anomaly detection for network incursions Federated deep neural network True negative rate of 97.9%, true positive rate of 99.7%, and accuracy of 99.7% Many studies have considered applications to detect security threats such as network attacks [116], network intrusion [118], anomaly [120,127], network intrusion [121,124], malware [122], and attack paths [123]. More discussion is presented on security threats and common tools in Section 5.…”

Section: Applicationsmentioning

confidence: 99%

“…To tackle security threats, automatic detection via machine learning algorithms [116,118,[120][121][122][123][124]127] is one promising solution. There are various cybersecurity tools to protect systems from cyber-attacks:…”

mentioning

confidence: 99%

See 3 more Smart Citations

A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions

Chui¹,

Gupta

Liu³

et al. 2023

Information

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The smart city vision has driven the rapid development and advancement of interconnected technologies using the Internet of Things (IoT) and cyber-physical systems (CPS). In this paper, various aspects of IoT and CPS in recent years (from 2013 to May 2023) are surveyed. It first begins with industry standards which ensure cost-effective solutions and interoperability. With ever-growing big data, tremendous undiscovered knowledge can be mined to be transformed into useful applications. Machine learning algorithms are taking the lead to achieve various target applications with formulations such as classification, clustering, regression, prediction, and anomaly detection. Notably, attention has shifted from traditional machine learning algorithms to advanced algorithms, including deep learning, transfer learning, and data generation algorithms, to provide more accurate models. In recent years, there has been an increasing need for advanced security techniques and defense strategies to detect and prevent the IoT and CPS from being attacked. Research challenges and future directions are summarized. We hope that more researchers can conduct more studies on the IoT and on CPS.

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Section: Recent Cps and Iot Applicationsmentioning

confidence: 99%

Section: Recent Cps and Iot Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

mentioning

confidence: 99%

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A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions

Chui¹,

Gupta

Liu³

et al. 2023

Information

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Convnext-Eesnn: An effective deep learning based malware detection in edge based IIOT

Maddali

2024

IFS

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A rising number of edge devices, like controllers, sensors, and robots, are crucial for Industrial Internet of Things (IIoT) networks for collecting data for communication, storage, and processing. The security of the IIoT could be compromised by any malicious or unusual behavior on the part of any of these devices. They may also make it possible for malicious software placed on end nodes to enter the network and perform unauthorized activities. Existing anomaly detection techniques are less effective due to the increasing diversity of the network and the complexity of cyberattacks. In addition, most strategies are ineffective for devices with limited resources. Therefore, this work presents an effective deep learning based Malware Detection framework to make the edge based IIoT network more secure. This multi-stage system begins with the Deep Convolutional Generative Adversarial Networks (DCGAN) based data augmentation method to overcome the issue of data imbalance. Next, a ConvNeXt-based method extracts the features from the input data. Finally, an optimized Enhanced Elman Spike Neural Network (EESNN) based deep learning is utilized for malware recognition and classification. Using two distinct datasets— MaleVis and Malimg— the generalizability of the suggested model is clearly demonstrated. With an accuracy of 99.24% for MaleVis and 99.31% for the Malimg dataset, the suggested strategy demonstrated excellent results and surpassed all other existing methods. It illustrates how the suggested strategy outperforms alternative models and offers numerous benefits.

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Deep learning-powered malware detection in cyberspace: a contemporary review

Redhu,

Choudhary,

Srinivasan

et al. 2024

Front. Phys.

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This article explores deep learning models in the field of malware detection in cyberspace, aiming to provide insights into their relevance and contributions. The primary objective of the study is to investigate the practical applications and effectiveness of deep learning models in detecting malware. By carefully analyzing the characteristics of malware samples, these models gain the ability to accurately categorize them into distinct families or types, enabling security researchers to swiftly identify and counter emerging threats. The PRISMA 2020 guidelines were used for paper selection and the time range of review study is January 2015 to Dec 2023. In the review, various deep learning models such as Recurrent Neural Networks, Deep Autoencoders, LSTM, Deep Neural Networks, Deep Belief Networks, Deep Convolutional Neural Networks, Deep Generative Models, Deep Boltzmann Machines, Deep Reinforcement Learning, Extreme Learning Machine, and others are thoroughly evaluated. It highlights their individual strengths and real-world applications in the domain of malware detection in cyberspace. The review also emphasizes that deep learning algorithms consistently demonstrate exceptional performance, exhibiting high accuracy and low false positive rates in real-world scenarios. Thus, this article aims to contribute to a better understanding of the capabilities and potential of deep learning models in enhancing cybersecurity efforts.

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Optimal feature selection for malware detection in cyber physical systems using graph convolutional network

Cited by 3 publications

References 12 publications

A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions

A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions

Convnext-Eesnn: An effective deep learning based malware detection in edge based IIOT

Deep learning-powered malware detection in cyberspace: a contemporary review

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