GADoT：基于GAN的对抗训练，用于稳健的DDoS攻击检测
GADoT: GAN-based Adversarial Training for Robust DDoS Attack Detection
摘要：
机器学习（ML）已被证明在许多应用领域是有效的。然而，ML方法可能容易受到对抗攻击的攻击，其中攻击者试图通过制作输入数据来欺骗分类/预测机制。在基于ML的网络入侵检测系统（NIDS）的情况下，攻击者可能会使用他们对入侵检测逻辑的了解来生成未被检测到的恶意流量。解决这个问题的一种方法是采用对抗性训练，其中训练集用对抗性流量样本进行扩充。本文提出了一种称为GADoT的对抗性训练方法，该方法利用生成对抗网络（GAN）生成对抗性DDoS样本进行训练。我们表明，一个国家的最先进的NIDS与流行的数据集上的高准确性可以经历超过60%的未检测到的恶意流量下对抗攻击。然后，我们展示了在使用GADoT进行对抗训练后，这个分数如何下降到1.8%或更低。
英文摘要：
Machine Learning (ML) has proven to be effective in many application domains. However, ML methods can be vulnerable to adversarial attacks, in which an attacker tries to fool the classification/prediction mechanism by crafting the input data. In the case of ML-based Network Intrusion Detection Systems (NIDSs), the attacker might use their knowledge of the intrusion detection logic to generate malicious traffic that remains undetected. One way to solve this issue is to adopt adversarial training, in which the training set is augmented with adversarial traffic samples. This paper presents an adversarial training approach called GADoT, which leverages a Generative Adversarial Network (GAN) to generate adversarial DDoS samples for training. We show that a state-of-the-art NIDS with high accuracy on popular datasets can experience more than 60% undetected malicious flows under adversarial attacks. We then demonstrate how this score drops to 1.8% or less after adversarial training using GADoT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
