基于数据挖掘的自适应入侵检测框架设计  

Design and Implementation of the Adaptive Intrusion Detection Framework Based on Data Mining

作　　者：刘永健[1] 徐昕[1] 王正华[1] 薛倡新[1] 

机构地区：[1]国防科学技术大学计算机学院,长沙410073

出　　处：《计算机工程与应用》2006年第14期152-154,共3页Computer Engineering and Applications

摘　　要：数据挖掘、人工神经网络和机器学习等技术在入侵检测中的广泛应用,大幅度地提高了检测引擎的精度,但误用检测中的漏报率和异常检测中的误报率仍然是入侵检测中的难题。论文结合误用检测和异常检测的特点,利用机器学习思想,设计实现了一种新型的具有自适应能力的复合式入侵检测系统。The widely use of data mining,artificial neural network and machine learning in the intrusion detection has greatly improved the precision of the detection engine.However,the missed alarms in the misuse detection and the false alarms in the anomaly detection are still big problems.This paper combines the advantages of the misuse detection and the anomaly detection,integrates the idea of machine learning,designs and implements a novel compound intrusion detection system with self-adaptive ability.

关 键 词：复合引擎 自适应 数据清洗 数据挖掘 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]