Adversarial Anomaly Detection

Considerable attention has been given to the vulnerability of machine learning to adversarial samples. This is particularly critical in anomaly detection; uses such as detecting fraud, intrusion, and malware must assume a malicious adversary. We specifically address poisoning attacks, where the adversary injects carefully crafted benign samples into the data, leading to concept drift that causes the anomaly detection to misclassify the actual attack as benign. Our goal is to estimate the vulnerability of an anomaly detection method to an unknown attack, in particular the expected

minimum number of poison samples the adversary would need to succeed. Such an estimate is a necessary step in risk analysis: do we expect the anomaly detection to be sufficiently robust to be useful in the face of attacks? We analyze DBSCAN, LOF,

one-class SVM as an anomaly detection method, and derive estimates for robustness to poisoning attacks. The analytical estimates are validated against the number of poison samples needed for the actual anomalies in standard anomaly detection test

datasets. We then develop defense mechanism, based on the concept drift caused by the poisonous points, to identify that an attack is underway. We show that while it is possible to detect the attacks, it leads to a degradation in the performance of the

anomaly detection method. Finally, we investigate whether the generated adversarial samples for one anomaly detection method transfer to another anomaly detection method.