A study on the feature selection of network traffic for intrusion detection purpose

IEEE International Conference on Intelligence and Security Informatics, 2008, IEEE ISI 2008

Volumn , Issue , 2008, Pages 245-247

  Ma, Wanli ^a,b   Tran, Dat ^a,b   Sharma, Dharmendra ^a,b  

^a IEEE

^b UNIVERSITY OF CANBERRA   (Australia)

Author keywords

Clustering methods; Feature extraction; Intrusion detection

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; COMPUTER CRIME; DATA MINING; DECISION SUPPORT SYSTEMS; FEATURE EXTRACTION; FINANCIAL DATA PROCESSING; FLOW OF SOLIDS; FUZZY CLUSTERING; INFORMATION MANAGEMENT; INTERNET; INTERSYMBOL INTERFERENCE; MINING; SEARCH ENGINES; SENSORS;

ANOMALY DETECTIONS; CLUSTERING METHODS; DATA MINING METHODS; DATA NORMALIZATION; DATA SETS; DATA VALUES; DATA-MINING ALGORITHMS; DETECTION RATES; FEATURE SELECTION; FUZZY C-MEANS CLUSTERING; FUZZY ENTROPY CLUSTERING; INTERNATIONAL CONFERENCES; INTRUSION DETECTION SYSTEMS; K-MEANS CLUSTERING; NETWORK DATA; NETWORK TRAFFICS; SECURITY INFORMATICS;

INTRUSION DETECTION;

EID: 51849135423     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISI.2008.4565069     Document Type: Conference Paper

References (11)

1. Chan, P.K., M.V. Mahoney, and M.H. Arshad, A Machine Learning Approach to Anomaly Detection. 2003, Florida Institute of Technology: Melbourne, FL, USA.
2. McHugh, J. The 1998 Lincoln Laboratory IDS Evaluation (A Critique). in Recent Advances in Intrusion Detection 2000 (RAID 2000). 2000. Toulouse, France: Springer.
3. Portnoy, L., E. Eskin, and S. Stolfo. Intrusion detection with unlabeled data using clustering. in Proceedings of ACM CSS Workshop on Data Mining Applied to Security (DMSA-2001). 2001. Philadelphia, PA, USA: ACM Press.
4. ACM. KDD CUP 1999 data. [cited 12 January 2007]; Available from: http://kdd.ics.uci.edU//databases/kddcup99/kddcup99.html.
5. Taylor, C. and J. Alves-Foss. An Empirical Analysis of NATE: Network Analysis of Anomalous Traffic Events. in 10th New Security Paradigms Workshop. 2002. Virginia Beach, Virginia, USA: ACM Press.
6. Taylor, C. and J. Alves-Foss. NATE: Network Analysis of Anomalous Traffic Events, a low-cost approach. in Proceedings of New Security Paradigms Workshop. 2001. Cloudcroft, New Mexico, USA.
7. DARPA. DARPA Intrusion Detection Evaluation Data Sets. 1999 [cited 2006 15 October 2006]; Available from: http://www.ll.mit.edu/IST/ideval/data/ data_index.html.
8. Li, X. and N. Ye. Mining Normal and Intrusive Activity Patterns for Computer Intrusion Detection. in Intelligence and Security Informatics: Second Symposium on Intelligence and Security Informatics. 2004. Tucson, AZ, USA: Springer-Verlag.
9. Caruso, C. and D. Malerba. Clustering as an add-on for firewalls. in Fifth International Conference on Data Mining, Text Mining and Their Business Applications (DATA MINING 2004). 2004. Malaga, Spain: WIT Press, Southampton, UK.
10. Wang, Q. and V. Megalooikonomou. A clustering algorithm for intrusion detection. in SPIE Conference on Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security. 2005. Orlando, Florida, USA.
11. Stolfo, S.J., W. Fan, et al. Cost-based Modeling and Evaluation for Data Mining With Application to Fraud and Intrusion Detection: Results from the JAM Project, in Proceedings of 2000 DARPA Information Survivability Conference and Exposition. 2000.