Active learning for network intrusion detection

Anomaly detection for network intrusion detection is usually considered an unsupervised task. Prominent techniques, such as one-class support vector machines, learn a hypersphere enclosing network data, mapped to a vector space, such that points outside of the ball are considered anomalous. However, this setup ignores relevant information such as expert and background knowledge. In this paper, we rephrase anomaly detection as an active learning task. We propose an effective active learning strategy to query lowconfidence observations and to expand the data basis with minimal labeling effort. Our empirical evaluation on network intrusion detection shows that our approach consistently outperforms existing methods in relevant scenarios. Categories and Subject Descriptors C.2.0 [Computer-Communication Networks]: General—Security and protection; I.2.6 [Artificial Intelligence]: Learning—Parameter learning; I.5.2 [Pattern Recognition]: Design Methodology—Classifier design and eva...

• Type: Conference
• Year: 2009
• Where: CCS
• Authors: Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf Brefeld
• Keywords: Active Learning, Anomaly Detection, CCS 2009, Network Intrusion Detection, Security Privacy