入侵检测分类引擎预测精度度量方法  

A New Measurement of Prediction Accuracy for Classification Engines in Intrusion Detection

作　　者：彭新光[1,2] 王峥 刘玉树[1] 吴裕树[1] 

机构地区：[1]北京理工大学计算机科学与工程系,北京100081 [2]原理工大学信息工程学院,太原030024

出　　处：《计算机工程》2004年第4期102-103,178,共3页Computer Engineering

摘　　要：在综合考虑入侵检测分类引擎对异常和正常记录整体预测能力的基础上，提出了一种与测试和目标审计记录集中的异常记录分布无关的预测精度度量方法。该度量方法消除了当前评估规范存在的缺陷，能够确保预测精度就是实际入侵检测时的泛化精度。A new measurement method used for predicting accuracy of classification engines in the intrusion detection system is proposed on the basis of integrated consideration to the predictive ability of classification engines to normal and abnormal records. The proposed measurement is independent of the distribution of abnormal records in test and real audit sets and completely eliminates deficiencies of the current criterions. The predictive accuracy is assuredly the generalization accuracy in the actual intrusion detection.

关 键 词：入侵检测 分类引擎 评估精度 泛化精度 度量规范 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]