基于机器学习方法的入侵检测技术的研究

入侵检测技术是近20年来才出现的一种有效保护网络系统免受网络攻击的新型网络安全技术.随着网络技术的迅速发展、安全问题的日益突出,传统的入侵检测系统已难以满足对越来越复杂的网络攻击的检测任务,将机器学习的技术引入到入侵监测系统之中以有效地提高系统性能,已成为入侵检测技术的研究热点.本文主要介绍了入侵检测系统的基本结构以及几种机器学习方法在入侵检测中的应用,其中包括:基于贝叶斯分类的方法、基于神经网络的方法、基于数据挖掘的方法与基于支持向量机的方法.

On Intrusion Detection Technology Based on Machine Learning Method

Intrusion Detection(ID)is a new emerging network security technology in the recent 20 years,which can protect the network system from the network attacks effectively.With the rapid development of the network technology and the fast increasing of intrusion problems,the traditional intrusion detection methods cannot work well with the more and more complicated intrusions.So introducing machine learning into intrusion detection systems to improve the performance has become one of the major concerns in the research of intrusion detection.This paper introduces the basic structure of the intrusion detection system and the application of machine learning in intrusion detection,including the Bayes-based method,the neural network-based method,the data mining-based method and the SVM-based method.