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DDoS Attack Simulation and Machine Learning-Based Detection Approach in Internet of Things Experimental Environment

DDoS Attack Simulation and Machine Learning-Based Detection Approach in Internet of Things Experimental Environment

Hongsong Chen, Caixia Meng, Jingjiu Chen
Copyright: © 2021 |Volume: 15 |Issue: 3 |Pages: 18
ISSN: 1930-1650|EISSN: 1930-1669|EISBN13: 9781799859888|DOI: 10.4018/IJISP.2021070101
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MLA

Chen, Hongsong, et al. "DDoS Attack Simulation and Machine Learning-Based Detection Approach in Internet of Things Experimental Environment." IJISP vol.15, no.3 2021: pp.1-18. http://doi.org/10.4018/IJISP.2021070101

APA

Chen, H., Meng, C., & Chen, J. (2021). DDoS Attack Simulation and Machine Learning-Based Detection Approach in Internet of Things Experimental Environment. International Journal of Information Security and Privacy (IJISP), 15(3), 1-18. http://doi.org/10.4018/IJISP.2021070101

Chicago

Chen, Hongsong, Caixia Meng, and Jingjiu Chen. "DDoS Attack Simulation and Machine Learning-Based Detection Approach in Internet of Things Experimental Environment," International Journal of Information Security and Privacy (IJISP) 15, no.3: 1-18. http://doi.org/10.4018/IJISP.2021070101

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Abstract

Aiming at the problem of DDoS attack detection in internet of things (IoT) environment, statistical and machine-learning algorithms are proposed to model and analyze the network traffic of DDoS attack. Docker-based virtualization platform is designed and configured to collect IoT network traffic data. Then the packet-level, flow-level, and second-level network traffic datasets are generated, and the importance of features in different traffic datasets are sorted. By SKlearn and TensorFlow machine-learning software framework, different machine learning algorithms are researched and compared. In packet-level DDoS attack detection, KNN algorithm achieves the best results; the accuracy is 92.8%. In flow-level DDoS attack detection, the voting algorithm achieves the best results; the accuracy is 99.8%. In second-level DDoS attack detection, the RNN algorithm behaves best results; the accuracy is 97.1%. The DDoS attack detection method combined with statistical analysis and machine-learning can effectively detect large-scale DDoS attacks on the internet of things simulation experimental environment.

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