Design and Implementation of a Deep Learning-Based Intrusion Detection System in Edge Computing 


Vol. 47,  No. 8, pp. 1114-1127, Aug.  2022
10.7840/kics.2022.47.8.1114


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  Abstract

Edge computing is a new distributed computing technology that adds an edge layer between the cloud and device layer. Edge computing offers more targets than cloud computing, and attackers exploit vulnerabilities, DoS/DDoS, man-in-the-middle attacks, and authentication bypasses to threaten them. Security systems such as intrusion detection systems (IDS), firewalls, and anti-virus software are unsuitable for edge computing due to low accuracy and high false positives. It also revealed limitations such as a lack of security personnel and solutions to respond. This paper proposes a deep learning-based IDS to overcome the limitations of edge computing. We implemented a deep learning-based IDS in KubeEdge that a highly scalable edge computing platform and extracted important features using sparsity constraints to train an intrusion detection model. The model deployed in edge computing achieved 98.96% accuracy, 99.41% F1-Score, 2.270% false-positive, and 0.4990% undetected rate. The system reported to the user that an intrusion had occurred and took appropriate actions to black the attackers" IP.

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  Cite this article

[IEEE Style]

J. Kim and M. Choi, "Design and Implementation of a Deep Learning-Based Intrusion Detection System in Edge Computing," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 8, pp. 1114-1127, 2022. DOI: 10.7840/kics.2022.47.8.1114.

[ACM Style]

Jong-Wouk Kim and Mi-Jung Choi. 2022. Design and Implementation of a Deep Learning-Based Intrusion Detection System in Edge Computing. The Journal of Korean Institute of Communications and Information Sciences, 47, 8, (2022), 1114-1127. DOI: 10.7840/kics.2022.47.8.1114.

[KICS Style]

Jong-Wouk Kim and Mi-Jung Choi, "Design and Implementation of a Deep Learning-Based Intrusion Detection System in Edge Computing," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 8, pp. 1114-1127, 8. 2022. (https://doi.org/10.7840/kics.2022.47.8.1114)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880