Evading Anomaly Detection through Variance Injection Attacks on PCA (Extended Abstract)
Benjamin I. P. Rubinstein, Blaine A. Nelson, Ling Huang, Anthony Douglas Joseph, Shing-hon Lau, Nina Taft, J. D. Tygar · 2008
Abstract. Whenever machine learning is applied to security problems, it is important to measure vulnerabilities to adversaries who poison the training data. We demonstrate the impact of variance injection schemes on PCA-based network-wide volume anomaly detectors, when a single compromised PoP injects chaff into the network. These schemes can increase the chance of evading detection by sixfold, for DoS attacks. 1