论文标题
GADOT：基于GAN的对抗训练，用于鲁棒DDOS攻击检测
GADoT: GAN-based Adversarial Training for Robust DDoS Attack Detection
论文作者
论文摘要
事实证明，机器学习（ML）在许多应用领域都有效。但是，ML方法可能容易受到对抗攻击的影响，在这种攻击中，攻击者试图通过制定输入数据来欺骗分类/预测机制。对于基于ML的网络入侵检测系统（NIDSS），攻击者可能会利用他们对入侵检测逻辑的了解来产生未被发现的恶意流量。解决此问题的一种方法是采用对抗性培训，其中训练集通过对抗性交通样本进行增强。本文提出了一种称为Gadot的对抗训练方法，该方法利用生成的对抗网络（GAN）生成对抗性DDOS样品进行培训。我们表明，在受欢迎的数据集上具有很高精度的最先进的NID可以在对抗性攻击下经历60％以上未发现的恶意流。然后，我们证明了使用Gadot在对抗训练后如何下降到1.8％或以下。
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.
