Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines

Abstract

Anomaly detection discovers regular patterns in unlabeled data and identifies the non-conforming data points, which in some cases are the result of malicious attacks by adversaries. Learners such as One-Class Support Vector Machines (OCSVMs) have been successfully used in anomaly detection, yet their performance may degrade significantly in adversarial conditions such as integrity attacks. This work focuses on integrity attacks, where the adversary distorts the training data in order to successfully avoid detection during evaluation. This paper presents a unique combination of anomaly detection using (1) OCSVMs in the presence of adversaries who distort training data in a targeted manner a..