Evading Anomaly Detection through Variance Injection Attacks on PCA

Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Nina Taft, J. Doug Tygar

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.