Unexpected-behavior detection using TopK rankings for cybersecurity

Abstract

Anomaly-based intrusion detection systems use profiles to characterize expected behavior of network users. Most of these systems characterize the entire network traffic within a single profile. This work proposes a user-level anomaly-based intrusion detection methodology using only the user's network traffic. The proposed profile is a collection of TopK rankings of reached services by the user. To detect unexpected behaviors, the real-time traffic is organized into TopK rankings and compared to the profile using similarity measures. The experiments demonstrated that the proposed methodology was capable of detecting a particular kind of malware attack in all the users tested.