【摘要】 将基于HOPCOUNT的异常数据包过滤技术引入到TaoPeng等人提出的检测方法中,提出了一个新型的DDoS攻击的检测模型.通过判定算法,该模型能够较为准确的区分出正常通信量和异常通信量,并在此基础上,运用CUSUM算法监测两个特征量,实现了DDoS攻击检测.此外,本文将Bloom Filter算法引入到数据库的查找过程中,提高了检测的性能以及检测模型自身的安全性.实验结果证明,该检测模型能够以较高的精确度及时的检测出DDoS攻击行为.更多还原

【Abstract】 This paper,we propose a new DDoS detection model by introducing the abnormal packet filtering based on HOP COUNT into the Tao Peng’s DDoS detection method. The proposed model can differentiate the normal traffics from abnormal traffics by a determinant algorithm. On the basis we implement DDoS attack detection using the CUSUM algorithm to inspect two detection features. Furthermore, we introduce the Bloom Filter algorithm into the database lookup processing, which can improve the detection performance and self-security. The experiment demonstrates this model can detect DDoS attack as early as possible with high detection accuracy.更多还原