A Contextual Anomaly Detection Approach to Discover Zero-Day Attacks

Abstract

There is a considerable interest in developing techniques to detect zero-day (unknown) cyber-attacks, and considering context is a promising approach. This paper describes a contextual misuse approach combined with an anomaly detection technique to detect zero-day cyber attacks. The contextual misuse detection utilizes similarity with attack context profiles, and the anomaly detection technique identifies new types of attacks using the One Class Nearest Neighbor (1-NN) algorithm. Experimental results on the NSL-KDD intrusion detection dataset have shown that the proposed approach is quite effective in detecting zero-day attacks.