Use of K-Nearest Neighbor classifier for intrusion detection

被引：469

作者：

Liao, YH ^{[1
]}

Vemuri, VR ^{[1
]}

机构：

[1] Univ Calif Davis, Dept Comp Sci, Davis, CA 95616 USA

来源：

COMPUTERS & SECURITY | 2002年 / 21卷 / 05期

关键词：

k-Nearest Neighbor classifier; intrusion detection; system calls; text categorization; program profile;

D O I：

10.1016/S0167-4048(02)00514-X

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new approach, based on the k-Nearest Neighbor (kNN) classifier, is used to classify program behavior as normal or intrusive. Program behavior, in turn, is represented by frequencies of system calls. Each system call is treated as a word and the collection of system calls over each program execution as a document. These documents are then classified using kNN classifier, a popular method in text categorization. This method seems to offer some computational advantages over those that seek to characterize program behavior with short sequences of system calls and generate individual program profiles. Preliminary experiments with 1998 DARPA BSM audit data show that the kNN classifier can effectively detect intrusive attacks and achieve a low false Positive rate.

引用

页码：439 / 448

页数：10

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