Improving one-class SVM for anomaly detection

International Conference on Machine Learning and Cybernetics

Volumn 5, Issue , 2003, Pages 3077-3081

  Li, Kun Lun ^a,b   Huang, Hou Kuan ^a   Tian, Sheng Feng ^a   Xu, Wei ^a  

^a BEIJING JIAOTONG UNIVERSITY   (China)

^b HEBEI UNIVERSITY   (China)

Author keywords

Anomaly detection and SVMs; Information security; Intrusion detection

Indexed keywords

COMPUTER PROGRAMMING; COMPUTER SYSTEMS; COMPUTER VIRUSES; DATA REDUCTION; INFORMATION MANAGEMENT; PATTERN RECOGNITION;

ANOMALY DETECTION; INTRUSION DETECTION; SUPPORT VECTOR MACHINES (SVM);

SECURITY OF DATA;

EID: 1542284996     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Wenke Lee, Salvatore J. Stolfo & Kui W. Moka. Adaptive Intrusion Detection: A Data Mining Approach. Artificial Intelligence Review 14: 533-567, 2000
2. Bernhard Schölkopf et al. Estimating the support of a High-Dimensional Distribution. Technical Report, Department of Computer Science, University of Haifa, Haifa, 2001
3. Larry M. Manevitz and Malik Yousef. One-class SVMs for Document Classification. JMLR,2, 2001
4. V. N. Vapnik. The Nature of Statistical Learning Theory. New York: Springer. 1995
5. Jake Ryan, Meng-Jang Lin, Risto Miikkulainen. Intrusion Detection with Neural Networks, Advances in Neural Information Processing Systems, 1998
6. Srinivas Mukkarmala, Guadalupe Janoski and Andrew Sung. Intrusion Detection Using Neural Networks and Support Vector Machines, IEEE IJCNN (May, 2002).
7. Yunqiang Chen, Xiang Zhou, and Thomas S. Huang. One-Class SVM for Learning in Image Retrieval, Proceeding IEEE Int'l conference on Image processing, 2001
8. James Cannady. Artificial Neural Networks for Misuse Detection, Proceedings of the 1998 National Information Systems Security Conference (NISSC'98) Arlington, VA, October 5-8 1998
9. http://kdd.ics.uci.edu/databases/kddcup99
10. Kun-Lun Li, Hou-Kuan Huang, Sheng-Feng Tian. A Novle Multi-class SVM Classifier Based on DDAG. IEEE ICMLC'02 , Nov, 2002