Comparative Evaluation of Anomaly Detection Techniques for Sequence Data

被引：94

作者：

Chandola, Varun ^{[1
]}

Mithal, Varun ^{[2
]}

Kumar, Vipin ^{[1
]}

机构：

[1] Univ Minnesota, Minneapolis, MN 55455 USA

[2] IIT Kanpur, Kanpur, Uttar Pradesh, India

来源：

ICDM 2008: EIGHTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/ICDM.2008.151

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a comparative evaluation of a large number of anomaly detection techniques on a variety of publicly available as well as artificially generated data sets. Mangy of these are existing techniques while some are slight variants and/or adaptations of traditional anomaly detection techniques to sequence data.

引用

页码：743 / +

页数：2

共 18 条

[1]

Bateman A, 2002, NUCLEIC ACIDS RES, V30, P276, DOI [10.1093/nar/gkr1065, 10.1093/nar/gkp985, 10.1093/nar/gkh121]

[2]

BUDALAKOTI S, 2006, TM2006214553 NASA AM

[3]

CHANDOLA V, 2008, ACM COMPUTI IN PRESS

[4]

CHANDOLA V, 2008, 08021 U MINN

[5]

Cohen W. W., 1995, P 12 INT C MACH LEAR, P115, DOI DOI 10.1016/B978-1-55860-377-6.50023-2

[6]

Forrest S., 1996, SEC PRIV 1996 P 1996, P120, DOI [DOI 10.1109/SECPRI.1996.502675, 10.1109/SECPRI.1996.502675]

[7]

GAO B, 2002, ICMLC, P381

[8]

HOFMEYR S, 1998, J COMPUTER SECURITY, V6

[9]

Jain AK., 1988, Algorithms for clustering data

[10]

LEE W, 1997, AAAI WORKSH AI METH

← 1 2 →