Low-Rate DDoS Attacks Detection and Traceback by Using New Information Metrics

TLDR

Low‑rate DDoS attacks blend with normal traffic, evade anomaly‑based detection, and information metrics can quantify traffic differences. The paper proposes using two new information metrics—generalized entropy and information distance—to detect low‑rate DDoS attacks. These metrics measure the difference between legitimate and attack traffic to enable detection. The metrics detect attacks earlier than traditional measures, lower false positives, and the IP traceback algorithm accurately identifies all attackers.

Abstract

A low-rate distributed denial of service (DDoS) attack has significant ability of concealing its traffic because it is very much like normal traffic. It has the capacity to elude the current anomaly-based detection schemes. An information metric can quantify the differences of network traffic with various probability distributions. In this paper, we innovatively propose using two new information metrics such as the generalized entropy metric and the information distance metric to detect low-rate DDoS attacks by measuring the difference between legitimate traffic and attack traffic. The proposed generalized entropy metric can detect attacks several hops earlier (three hops earlier while the order α = 10 ) than the traditional Shannon metric. The proposed information distance metric outperforms (six hops earlier while the order α = 10) the popular Kullback-Leibler divergence approach as it can clearly enlarge the adjudication distance and then obtain the optimal detection sensitivity. The experimental results show that the proposed information metrics can effectively detect low-rate DDoS attacks and clearly reduce the false positive rate. Furthermore, the proposed IP traceback algorithm can find all attacks as well as attackers from their own local area networks (LANs) and discard attack traffic.