Detecting Method for DDoS Attack Based on Variance Analysis of Hurst Exponent
Shirui Zhu · Jisuanji gongcheng · 2008
This paper studies the change of the self-similarity caused by DDoS attack, and proposes a method to detect DDoS attack by calculating the variance of Hurst exponent. An experiment with the dataset of MIT Lincoln Laboratory is conducted to obtain the adjust criterion of DDoS attack. It shows that the proposed method can detect DDoS attack caused by changed variance of Hurst exponent and has higher detection efficiency. Its detection rate is 8% higher than the traditional method of feature matching, while the false alarm rate is 3% lower than the self-similar detecting method.