Network Intrusion Detection with Threat Agent Profiling

Abstract EN

With the increase in usage of computer systems and computer networks, the problem of intrusion detection in network security has become an important issue.

In this paper, we discuss approaches that simplify network administrator’s work.

We applied clustering methods for security incident profiling.

We consider K-means, PAM, and CLARA clustering algorithms.

For this purpose, we used data collected in Warden system from various security tools.

We do not aim to differentiate between normal and abnormal network traffic, but we focus on grouping similar threat agents based on attributes of security events.

We suggest a case of a fine classification and a case of a coarse classification and discuss advantages of both cases.