基于自相似的异常流量检测模型

现行网络中存在诸多影响网络安全和服务性能的异常流量,异常流量的存在不仅影响用户的正常使用,而且会造成网络拥塞和网络瘫痪,甚至会篡改和破坏用户及服务器的数据,造成不可估量的损失。为及时发现这些流量,设计了一个基于自相似特性的异常流量检测模型。根据现行网络流量大速度快等特点,该模型设计分为简单流分类模块、自适应抽样模块、实时估计Hurst参数模块以及异常流量判断模块四部分。设计的此检测模型能够在很大程度上保证网络流量检测的准确性和高效性。

Anomalous-traffic Detection Model based on Self-similarity

Various anomalous traffics have serious impacts on the safety and service performance of the modern network.The anomalous traffics in the network not only affects the normal use of the user,but also could cause network congestion paralysis,or more seriously,distort or destroy the data of the user and the servers,thus resulting in immeasurable losses.In order to find these anomalous traffics timely,an anomalous traffic detection model based on self-similarity is designed.According to the large-flow and high-speed characteristics of the modern network,the model consists of the simple flow classification module,the adaptive sampling module,the Hurst parameter on-line estimation and the anomalous-traffic judgment module.To a great extent this detection model could guarantee the accuracy and high efficiency of the network traffic detection.