面向网络流量入侵检测系统的黑盒攻击

A Black Box Attack on a Network Traffic Intrusion Detection System

基于机器学习的网络流量检测系统是网络安全领域现阶段比较热门的研究方向,但同时网络流量检测系统又受到了巨大挑战,因为攻击样本的生成,使该检测系统对恶意流量的检测性能降低。使用生成对抗网络生成对抗样本,通过在原始恶意流量中加入噪声干扰,即在攻击特征中加入不影响原始流量特性的非定向扰动,来实现扰乱检测模型的判断,从而躲过特征检测,将流量检测出的准确率降低了83.4%,为入侵检测模型提升自身鲁棒性提供了更为丰富的训练样本。

The network traffic detection system based on machine learning is a hot research direction in the field of network security at this stage,but at the same time,the network traffic detection system has been greatly challenged.The generation of attack samples reduces the detection performance of the detection system for malicious traffic.This article uses the generative adversarial network to generate adversarial samples,by adding noise interference to the original malicious traffic,so that non-directional disturbances that do not affect the characteristics of the original traffic are added to the attack characteristics to realize the judgment of the jamming detection model,thereby avoiding the characteristics detection and reducing the accuracy of traffic detection by 83.4%.It provides a richer training sample for the intrusion detection model to improve its robustness.