Masquerader detection using OCLEP: One-class classification using length statistics of emerging patterns

Seventh International Conference on Web-Age Information Management Workshops, WAIM 2006

Volumn , Issue , 2006, Pages

  Chen, Lijun ^a   Dong, Guozhu ^a  

^a WRIGHT STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN MATCHING; RADAR SYSTEMS; STATISTICS; USER INTERFACES;

COMPLEMENTARY COMPONENTS; MASQUERADER DETECTION SYSTEMS;

TARGET TRACKING;

EID: 35348917347     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WAIMW.2006.19     Document Type: Conference Paper

References (24)

1. C. C. Aggarwal and P. S. Yu. Outlier detection for high dimensional data. In Proceedings of the ACM SIGMOD international conference on Management of data, pages 37-46, New York, NY, USA, 2001. ACM Press.
2. H. Alhammady and K. Ramamohanarao. Using emerging patterns and decision trees in rare-class classification. In Proceedings of the IEEE International Conference on Data Mining, 2004.
3. H. Alhammady and K. Ramamohanarao. Expanding the training data space using emerging patterns and genetic methods. In Proceeding of the SIAM International Conference on Data Mining, 2005.
4. S. Axelsson. Intrusion detection systems: A survey and taxonomy. Technical Report 99-15, Chalmers Univ., March 2000.
5. J. Bailey, T. Manoukian, and K. Ramamohanarao. A fast algorithm for computing hypergraph transversals and its application in mining emerging patterns. In Proceedings of IEEE ICDM, 2003.
6. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software at http://www.csie.ntu.edu.tw/cjlin/libsvm.
7. CSI/FBI. CSI/FBI Computer Crime and Security Survey., 2003.
8. G. Dong and J. Li. Efficient mining of emerging patterns: Discovering trends and differences. In Proc. of the 5th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, 1999.
9. G. Dong, J. Li, and L. Wong. The use of emerging patterns in the analysis of gene expression profiles for the diagnosis and understanding of diseases. In M. Kantardzic and J. Zurada, editors, New Generation of Data Mining Applications. IEEE Press, 2005.
10. G. Dong, X. Zhang, L. Wong, and J. Li. CAEP: Classification by aggregating emerging patterns. In Proc. 2nd Int'l Conf. on Discovery Science, Tokyo, Japan, 1999.
11. W. DuMouchel, W.-H. Ju, A. F. Karr, M. Schonlau, M. Theusan, and Y. Vardi. Computer intrusion: Detecting masquerades. Statistical Science, 16(1):1-17, 2001.
12. E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo. A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data. Data Mining for Security Applications. Kluwer., 2002.
13. H. Fan and K. Ramamohanarao. A bayesian approach to use emerging patterns for classification. In Proceedings of the 14th Australasian Database Conference, 2003.
14. S. Greenberg. Using Unix: Collected traces of 168 users. ResearchReport 88/333/45, Department of Computer Science, University of Calgary, 1988.
15. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2000.
16. J. Li, G. Dong, K. Ramamohanarao, and L. Wong. DeEPs: A new instance-based lazy discovery and classification system. Machine Learning, 2002.
17. J. Li, H. Liu, J. R. Downing, A. E.-J. Yeoh, and L. Wong. Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients. In Bioinformatics, pages 19:71-78, 2003.
18. L. M. Manevitz and M. Yousef. One-class svms for document classification. J. Mach. Learn. Res., 2:139-154, 2002.
19. R. A. Maxion. Masquerade detection using enriched command lines. In Proceedings of the International Conference on Dependable Systems and Networks, pages 5-14, San Francisco, CA, USA, 2003. IEEE Computer Society.
20. R. A. Maxion and T. N. Townsend. Masquerade detection using truncated command lines. In Proceedings of the International Conference on Dependable Systems and Networks, pages 219-228, Washington, DC, USA, 2002. IEEE Computer Society.
21. V. N. Vapnik. Statistical learning theory. Wiley, 1998.
22. K. Wang and S. J. Stolfo. One Class Training for Masquerade Detection. ICDM Workshop on Data Mining for Computer Security (DMSEC 03)., 2003.
23. H. Yu, J. Han, and K. C.-C. Chang. Pebl: positive example based learning for web page classification using svm. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 239-248, New York, NY, USA, 2002. ACM Press.
24. X. Zhang, G. Dong, and K. Ramamohanarao. Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets. In KDD, pages 310-314, 2000.