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IMC
2009
ACM

ANTIDOTE: understanding and defending against poisoning of anomaly detectors

9 years 3 months ago
ANTIDOTE: understanding and defending against poisoning of anomaly detectors
Statistical machine learning techniques have recently garnered increased popularity as a means to improve network design and security. For intrusion detection, such methods build a model for normal behavior from training data and detect attacks as deviations from that model. This process invites adversaries to manipulate the training data so that the learned model fails to detect subsequent attacks. We evaluate poisoning techniques and develop a defense, in the context of a particular anomaly detector—namely the PCA-subspace method for detecting anomalies in backbone networks. For three poisoning schemes, we show how attackers can substantially increase their chance of successfully evading detection by only adding moderate amounts of poisoned data. Moreover such poisoning throws off the balance between false positives and false negatives thereby dramatically reducing the efficacy of the detector. To combat these poisoning activities, we propose an antidote based on techniques from ...
Benjamin I. P. Rubinstein, Blaine Nelson, Ling Hua
Added 28 May 2010
Updated 28 May 2010
Type Conference
Year 2009
Where IMC
Authors Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft, J. D. Tygar
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