Affiliations: [a] University of Electronic Science and Technology of China, Chengdu, Sichuan, China | [b] CATARC Automotive Test Center (Tianjin) Co., Ltd, Tianjin, China | [c] Chengdu Weichen Information Technology Co., Ltd, Chengdu, Sichuan, China
Correspondence:
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Corresponding author: Luo Lei, University of Electronic Science and Technology of China, School of Computer Science and Engineering, B1-307, Chengdu, Sichuan, China. E-mail: lluo@uestc.edu.cn.
Abstract: In cybersecurity, intrusion detection systems (IDSes) are of vital importance, allowing different companies and their departments to identify malicious attacks from magnanimous network traffic; however, the effectiveness and stability of these artificial intelligence-based systems are challenged when coping with adversarial attacks. This work explores a creative framework based on a generative adversarial network (GAN) with a series of training algorithms that aims to generate instances of adversarial attacks and utilize them to help establish a new IDS based on a neural network that can replace the old IDS without knowledge of any of its parameters. Furthermore, to verify the quality of the generated attacks, a transfer mechanism is proposed for calculating the Frechet inception distance (FID). Experiments show that based on the original CICIDS2017 dataset, the proposed framework can generate four types of adversarial attacks (DDoS, DoS, Bruteforce, and Infiltration), which precipitate four types of classifiers (Decision Tree, Random Forest, Adaboost, and Deep Neural Network), set as black-box old IDSes, with low detection rates; additionally, the IDSes that the proposed framework newly establish have an average detection rate of 98% in coping with both generated adversarial and original attacks.
Keywords: Adversarial attacks, deep learning (DL), generative adversarial networks (GAN), intrusion detection system (IDS), machine learning (ML)
