一句话：用搜索式训练攻击测试IDS并协同演化防御，使系统对50个独立攻击中的49个具备韧性。
基于机器学习的异常检测系统易受训练攻击：攻击者逐步注入少量异常数据，悄然偏移学习状态，使有害数据得以通过。本文聚焦入侵检测系统（IDS）场景下此类攻击的系统化测试，提出一种基于搜索的方法自动生成训练攻击，并进一步搜索对策，从成功攻击中学习以提升被测试IDS的韧性。作者在拒绝服务攻击检测场景和真实网络流量数据集上评估该方法。实验表明，搜索式攻击能绕过当前最优防御，可为IDS所有配置状态生成攻击模式，并能给出适当对策；通过攻击与防御协同演化，IDS最终能抵御50个独立生成的攻击中的49个。
Machine-learning-based anomaly detection systems can be vulnerable to new kinds of deceptions, known as training attacks, which exploit the live learning mechanism of these systems by progressively injecting small portions of abnormal data. The injected data seamlessly swift the learned states to a point where harmful data can pass unnoticed. We focus on the systematic testing of these attacks in the context of intrusion detection systems (IDS). We propose a search-based approach to test IDS by making training attacks. Going a step further, we also propose searching for countermeasures, learning from the successful attacks and thereby increasing the resilience of the tested IDS. We evaluate our approach on a denial-of-service attack detection scenario and a dataset recording the network traffic of a real-world system. Our experiments show that our search-based attack scheme generates successful attacks bypassing the current state-of-the-art defences. We also show that our approach is capable of generating attack patterns for all configuration states of the studied IDS and that it is capable of providing appropriate countermeasures. By co-evolving our attack and defence mechanisms we succeeded at improving the defence of the IDS under test by making it resilient to 49 out of 50 independently generated attacks.
@inproceedings{paperbot5362,
title = {Search-based test and improvement of machine-learning-based anomaly detection systems},
author = {Maxime Cordy and Steve Muller and Mike Papadakis and Yves Le Traon},
booktitle = {ISSTA},
year = {2019},
doi = {10.1145/3293882.3330580}
}