An empirical evaluation of information metrics for low-rate and high-rate DDoS attack detection

摘要

Distributed Denial of Service (DDoS) attacks represent a major threat to uninterrupted and efficient Internet service. In this paper, we empirically evaluate several major information metrics, namely, Hartley entropy, Shannon entropy, Renyi's entropy, generalized entropy, Kullback-Leibler divergence and generalized information distance measure in their ability to detect both low-rate and high-rate DDoS attacks. These metrics can be used to describe characteristics of network traffic data and an appropriate metric facilitates building an effective model to detect both low-rate and high-rate DDoS attacks. We use MIT Lincoln Laboratory, CAIDA and TUIDS DDoS datasets to illustrate the efficiency and effectiveness of each metric for DDoS detection.