支持向量机在入侵检测中的应用

随着计算机技术技术的发展,入侵检测系统问题更加突出和复杂,它是任何一个完整的网络安全系统中必不可缺的部分。目前现有商用的入侵检测系统所采用的检测方法大多是基于规则的,这样就造成了它们只能够对某一些特定的或己知的入侵行为取得比较好的结果.本文把模糊C均值聚类和支持向量机结合到一起,支持向量机是一种高效的模式识别方式,为了降低它的运算复杂性,用FCM把输入数据进行适当处理。提高了系统整体入侵检测的运算速度和精度。通过实验证明了该方法具有一定的优势。

Application of the network intrusion detection based on support vector machine

Intrusion detection system must be needed in any integral complete security network system.The traditional detection methods are always signature-based detection.These methods extract features only from various audit streams,or detect intrusions by comparing the feature values to a set of attack signatures provided by human experts.The text combines support vector machine and fuzzy c-means clustering algorithm.Support vector machine is an efficacy technique of pattern recognition.FCM disposes data to reduce its complicacy of calculate,and operation rate and precision of Network Intrusion Detection system can be improved.The experiment proves that the method is superior over others.