计算机技术与发展2016，Vol.26Issue(3)：31-35,5.DOI:10.3969/j.issn.1673-629X.2016.03.008
基于熵和SVM多分类器的异常流量检测方法
An Anomaly Detection Method Based on Entropy and SVM Multi-class Classifier
摘要
Abstract
With the advent of the age of big data,data mining and machine learning methods have gradually replaced the traditional meth-ods of anomaly detection,which have gained more attention. In this paper,a new method of detecting the anomaly traffic based on the in-formation entropy and SVM is proposed. This method transfers anomaly detection problems into the classification of different types of traffic,and uses information entropy to quantify different attributes of network traffic. It puts forward an improved SVM multi-class clas-sifier to classify the entropy-quantified traffic and judges the anomalies accordingly. This method is implemented into a real system and function test is carried out. The results show that the method has a good detection effect for the abnormal traffic of the Internet.关键词
异常检测/信息熵/一对其余/分类Key words
anomaly detection/information entropy/one-to-all/classification分类
信息技术与安全科学引用本文复制引用
朱佳佳,陈佳..基于熵和SVM多分类器的异常流量检测方法[J].计算机技术与发展,2016,26(3):31-35,5.基金项目
国家重大专项(2013ZX03006002) （2013ZX03006002）
国家自然科学基金资助项目(61471029) （61471029）
北京市自然基金“面上”项目(4132053) （4132053）
基本科研业务费(2014JBM012) （2014JBM012）
