An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments

Lei Chen; Ru Huo; Tao Huang

doi:10.3390/a16040197

journal article Open Access Apr 05, 2023

An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments

Lei Chen

Ru Huo Tao Huang

Algorithms Vol. 16 No. 4 pp. 197 · MDPI AG

View at Publisher Save 10.3390/a16040197

Abstract

As an essential piece of infrastructure supporting cyberspace security technology verification, network weapons and equipment testing, attack defense confrontation drills, and network risk assessment, Cyber Range is exceptionally vulnerable to distributed denial of service (DDoS) attacks from three malicious parties. Moreover, some attackers try to fool the classification/prediction mechanism by crafting the input data to create adversarial attacks, which is hard to defend for ML-based Network Intrusion Detection Systems (NIDSs). This paper proposes an adversarial DBN-LSTM method for detecting and defending against DDoS attacks in SDN environments, which applies generative adversarial networks (GAN) as well as deep belief networks and long short-term memory (DBN-LSTM) to make the system less sensitive to adversarial attacks and faster feature extraction. We conducted the experiments using the public dataset CICDDoS 2019. The experimental results demonstrated that our method efficiently detected up-to-date common types of DDoS attacks compared to other approaches.

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Metrics

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Citations

24

References

Details

Published: Apr 05, 2023
Vol/Issue: 16(4)
Pages: 197
License: View

Authors

L

Lei Chen

College of Compute, National University of Defense Technology, Changsha 410073, China; Center for Strategic Studies, Chinese Academy of Engineering, Beijing 100088, China

R

Ru Huo

Purple Mountain Laboratories, Nanjing 211111, China; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

T

Tao Huang

The State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Funding

National Key R&D Program of China Award: 2019YFB1804403

Cite This Article

Lei Chen, Ru Huo, Tao Huang (2023). An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments. Algorithms, 16(4), 197. https://doi.org/10.3390/a16040197

An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments

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