네트워크 트래픽 분포 엔트로피를 이용한 비정상행위 탐지 방법

Abstract

Hostile network traffic is often different from normal traffic in ways that can be distinguished without knowing the exact nature of the attack. In this paper, we propose a new anomaly detection method using inbound network traffic distributions. For this purpose, we first characterize the traffic of a real campus network by the distributions of IP protocols, packet length, destination IP/port addresses, TTL value, TCP SYN packet, and fragment packet. And then we introduce the concept of entropy to transform the obtained baseline traffic distributions into manageable values. Finally, we can detect the anomalies by the difference of entropies between the current and baseline distributions. In particular, we apply the well-known denial-of-service attacks to a real campus network and show the experimental results.