A New, Principled Approach to Anomaly Detection

被引:12
作者
Ferragut, Erik M. [1 ]
Laska, Jason [1 ]
Bridges, Robert A. [1 ]
机构
[1] Oak Ridge Natl Lab, Computat Sci & Engn Div, Oak Ridge, TN 37830 USA
来源
2012 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2012), VOL 2 | 2012年
关键词
D O I
10.1109/ICMLA.2012.151
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Intrusion detection is often described as having two main approaches: signature-based and anomaly-based. We argue that only unsupervised methods are suitable for detecting anomalies. However, there has been a tendency in the literature to conflate the notion of an anomaly with the notion of a malicious event. As a result, the methods used to discover anomalies have typically been ad hoc, making it nearly impossible to systematically compare between models or regulate the number of alerts. We propose a new, principled approach to anomaly detection that addresses the main shortcomings of ad hoc approaches. We provide both theoretical and cyber-specific examples to demonstrate the benefits of our more principled approach.
引用
收藏
页码:210 / 215
页数:6
相关论文
共 8 条
[1]  
[Anonymous], 2007, ADV NEURAL INFORM PR
[2]  
Cao Y., 2009, P SOC PHOTO-OPT INS, V7480
[3]  
Cook K., 2012, VAST CHALLENGE 2012
[4]   AN INTRUSION-DETECTION MODEL [J].
DENNING, DE .
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 1987, 13 (02) :222-232
[5]  
Gu G., 2008, USENIX Security Symposium, V5, P139
[6]  
Portnoy L., 2001, Proc. ACM CSS Workshop on Data Mining Applied to Security (DMSA), P5
[7]   Anomaly Detection: A Survey [J].
Chandola, Varun ;
Banerjee, Arindam ;
Kumar, Vipin .
ACM COMPUTING SURVEYS, 2009, 41 (03)
[8]  
Tandon G., 2009, P 2009 SIAM INT C DA, P871, DOI [10.1137/1.9781611972795.75, DOI 10.1137/1.9781611972795.75]