基于大偏差统计模型的Http-Flood DDoS 检测机制及性能分析

针对Http洪泛Web DDoS(distributed denial of service)攻击,提出了一种检测机制.该机制首先采用型方法量化处理用户访问的网页序列,以得到用户访问不同网页的实际点击概率分布；然后,利用大偏差统计模型分析了用户访问行为的实际点击概率分布与网站先验概率分布的偏差；最后,依据大偏差概率检测恶意DDoS攻击.对该机制的性能进行仿真,结果表明,正常用户的大偏差概率大于恶意攻击者,并且大部分正常用户的大偏差概率大于10-36,而大部分恶意攻击者的大偏差概率则小于10-40由此,该机制能够有效地检测Http洪泛Web DDoS攻击,当检测门限设置为10-60时,其有效检测率可达97.5％,而误检率仅为0.6％.另外,将该机制与基于网页转移概率的检测方法进行性能比较,结果表明,该检测机制的检测率优于基于网页专业概率的检测机制,并且在误检率小于5％的情况下,该机制的检测率比现有检测机制提高0.6％.

Http-Flood DDoS Detection Scheme Based on Large Deviation and Performance Analysis

This paper focuses on Http-Flood DDoS(distributed denial of service) attack and proposes a detection scheme based on large deviation statistical model.The detection scheme characterizes the user access behavior with its Web-pages accessed and adopts the type method quantizing user’s access behavior.Based on this quantization method,this study analyzes the deviation of ongoing user’s empirical access behavior from the website’s priori one with large deviation statistical model,and detects Http-Flood DDoS with large deviation probability.This paper also provides preliminary simulation regarding the efficiency of the scheme,and the simulation results show that the large deviation of most normal Web surfers is larger than 10?36,yet,the attacker’s is smaller than 10?40.Thus,this scheme is promising to detect Http-Flood DDoS.Specifically,the scheme can achieve 0.6% false positive and 97.5% true positive with detection threshold of 10?60.And compared with the existing detection methods,this detection scheme can outperform them in detection performance.In particular,this scheme can improve the true positive ratio 0.6% over the transition probability based detection scheme with the false positive below 5%.