AdvPurRec: Strengthening Network Intrusion Detection with Diffusion Model Reconstruction Against Adversarial Attacks

The ongoing race between attackers and defenders in cybersecurity hinges on who makes the first move. Defenders have a significant advantage if they can anticipate and counteract attacks preemptively. However, if attackers are aware of the defenders’ strategies, meaningful defense becomes exceedingly challenging. In this paper, we propose a reactive defense technique that adapts to the presence of attacks and mitigates their effects. We introduce an adversarial purification defense technique that leverages the capabilities of a diffusion denoising probabilistic model to eliminate adversarial noise. Through training the purifier and classifier independently on clean examples, our defense remains robust against unseen attacks, making it an agnostic defense method. We rigorously evaluate our defense technique using network intrusion datasets, demonstrating its superiority over other state-of-the-art defense techniques in terms of both effectiveness and efficiency. Our results demonstrate the potential of this method to significantly enhance the resilience of network intrusion detection systems against adversarial threats. To ensure the reproducibility of our results, we have made our implementation publicly available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>