Adversarial Attacks for Intrusion Detection Based on Bus Traffic

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IEEE Network


A communication bus is used to transmit electronic signals between components, realize functional integration through information sharing, and improve system efficiency. The current research on intrusion detection based on bus traffic is mainly pertaining to machine learning or time logic detection. However, recent studies have shown that machine learning models perform poorly in defense of various adversarial attacks. In this article, we propose a method based on generative adversarial networks to transform normal traffic into adversarial and malicious ones. To be closer to reality, adversarial example generation models on two threat scenarios are proposed. At the same time, the distance metric L2 is introduced in the loss function to ensure the authenticity of the generated adversarial examples. To evaluate our method, we use the traffic generated by the model to various intrusion detection systems based on bus. Experimental results show that the model is effective because the detection rate of different intrusion detection models decreases after the traffic is processed. Thus, the traffic generated by our models can be used as training data to enhance the accuracy of intrusion detection systems. IEEE

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Data models, Generative adversarial networks, Intrusion detection, Ions, Protocols, Security, Training, Bayesian networks, Computer crime, Learning systems, Network security


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