Survey on Anomaly Detection using Data Mining Techniques

被引:319
作者
Agrawal, Shikha [1 ]
Agrawal, Jitendra [1 ]
机构
[1] Rajiv Gandhi Proudyogiki Vishwavidyalaya, Dept Comp Sci & Engn, Bhopal, India
来源
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 19TH ANNUAL CONFERENCE, KES-2015 | 2015年 / 60卷
关键词
Anomaly Detection; Clustering; Classification; Data Mining; Intrusion Detection System; INTRUSION DETECTION;
D O I
10.1016/j.procs.2015.08.220
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In the present world huge amounts of data are stored and transferred from one location to another. The data when transferred or stored is primed exposed to attack. Although various techniques or applications are available to protect data, loopholes exist. Thus to analyze data and to determine various kind of attack data mining techniques have emerged to make it less vulnerable. Anomaly detection uses these data mining techniques to detect the surprising behaviour hidden within data increasing the chances of being intruded or attacked. Various hybrid approaches have also been made in order to detect known and unknown attacks more accurately. This paper reviews various data mining techniques for anomaly detection to provide better understanding among the existing techniques that may help interested researchers to work future in this direction. (C) 2015 The Authors. Published by Elsevier B.V.
引用
收藏
页码:708 / 713
页数:6
相关论文
共 28 条
[1]   Hybrid Approach for Detection of Anomaly Network Traffic using Data Mining Techniques [J].
Agarwal, Basant ;
Mittal, Namita .
2ND INTERNATIONAL CONFERENCE ON COMMUNICATION, COMPUTING & SECURITY [ICCCS-2012], 2012, 1 :996-1003
[2]  
[Anonymous], 2012, 2012 8 INT C WIR COM
[3]  
[Anonymous], 2004, P 2004 ACM S APPL CO, DOI DOI 10.1145/967900.967989
[4]  
Berkhin P, 2006, GROUPING MULTIDIMENSIONAL DATA: RECENT ADVANCES IN CLUSTERING, P25
[5]  
Chauhan A., 2011, International Journal of Scientific Engineering Research, V2, P1
[6]  
Chitrakar R., 2012, P 2012 3 AS HIM INT, P1
[7]  
Dokas P., 2002, NSF Workshop on Next Generation Data Mining, Baltimore, MD, P21
[8]  
Farid D. M., 2010, INT J NETWORK SECURI, DOI DOI 10.5121/IJNSA.2010.2202
[9]  
Fu Song., 2012, International Conference on Advanced Data Mining and Applications, P726
[10]   Anomaly-based network intrusion detection: Techniques, systems and challenges [J].
Garcia-Teodoro, P. ;
Diaz-Verdejo, J. ;
Macia-Fernandez, G. ;
Vazquez, E. .
COMPUTERS & SECURITY, 2009, 28 (1-2) :18-28