Generalized Insider Attack Detection Implementation using NetFlow Data
This addresses the critical problem of detecting insider attacks in commercial networks, which currently lacks good solutions, though it appears incremental in approach.
The paper tackles insider attack detection in commercial networks by combining One-Class SVM and bi-clustering techniques on NetFlow data to reduce false positives, showing promising results on two real-world datasets.
Insider Attack Detection in commercial networks is a critical problem that does not have any good solutions at this current time. The problem is challenging due to the lack of visibility into live networks and a lack of a standard feature set to distinguish between different attacks. In this paper, we study an approach centered on using network data to identify attacks. Our work builds on unsupervised machine learning techniques such as One-Class SVM and bi-clustering as weak indicators of insider network attacks. We combine these techniques to limit the number of false positives to an acceptable level required for real-world deployments by using One-Class SVM to check for anomalies detected by the proposed Bi-clustering algorithm. We present a prototype implementation in Python and associated results for two different real-world representative data sets. We show that our approach is a promising tool for insider attack detection in realistic settings.