Sequence alignment for masquerade detection

Computational Statistics and Data Analysis

Volumn 52, Issue 8, 2008, Pages 4116-4131

  Coull, Scott E ^a   Szymanski, Boleslaw K ^b  

^a JOHNS HOPKINS UNIVERSITY   (United States)

^b RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BIOINFORMATICS; INFORMATION SYSTEMS; USER INTERFACES;

COMPUTER AUDIT DATA; MASQUERADE ATTACKS; MASQUERADING;

INTRUSION DETECTION;

EID: 41249096616     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2008.01.022     Document Type: Article

References (24)

1. Altschul S.F., Gish W., Miller W., Myers E.W., and Lipman D.J. Basic local alignment search tool. Journal of Molecular Biology 215 3 (1990) 403-410
2. Altschul S.F., Madden T.L., Schaffer A.A., Zhang J., Zhang Z., Miller W., and Lipman D.J. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Research 25 17 (1997) 3389-3402
3. Bray N., Dubchak I., and Pachter L. AVID: A Global Alignment Program (2003)
4. Coull, S., Branch, J., Szymanski, B., Breimer, E., 2003. Intrusion detection: A bioinformatics approach. In: Proceedings of the 19th Annual Computer Security Applications Conference, pp. 24-33
5. Dayhoff M.O., Schwartz R.M., and Orcutt B.C. A model of evolutionary change in proteins. Atlas of Protein Sequence and Structure 5 Suppl. 3 (1978) 345-352
6. Lane, T., Brodley, C.E., 1997. Sequence matching and learning in anomaly detection for computer security. In: Proceedings of the AAAI-97 Workshop: AI Approaches to Fraud Detection and Risk Management, vol. 49, pp. 43-49
7. Lane, T., Brodley, C.E., 1998. Approaches to online learning and concept drift for user identification in computer security. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 259-263
8. Lane T., and Brodley C.E. Temporal sequence learning and data reduction for anomaly detection. ACM Transactions on Information and System Security 2 3 (1999) 295-331
9. Maxion, R.A., 2003. Masquerade detection using enriched command lines. In: Proceedings of the International Conference on Dependable Systems and Networks, pp. 5-14
10. Maxion, R.A., Townsend, T.N., 2002. Masquerade detection using truncated command lines. In: Proceedings of the International Conference on Dependable Systems and Networks, pp. 219-228
11. Needleman S.B., and Wunsch C.D. A general method applicable to the search for similarity in the amino acid sequence of two proteins. Journal of Molecular Biology 48 (1970) 443-453
12. Pearson W.R., and Lipman D.J. Improved tools for biological sequence comparison. Proceedings of the National Academy of Sciences 85 8 (1988) 2444-2448
13. Schonlau M., DuMouchel W., Ju W.H., Karr A.F., Theus M., and Vardi Y. Computer intrusion: Detecting masquerades. Statistical Science 16 1 (2001) 58-74
14. Schonlau M., and Theus M. Detecting masquerades in intrusion detection based on unpopular commands. Information Processing Letters 76 1 (2000) 33-38
15. Smith T.F., and Waterman M.S. Identification of common molecular subsequences. Journal of Molecular Biology 147 (1981) 195-197
16. Szymanski, B.K., Zhang, Y., 2004. Recursive data mining for masquerade detection and author identification. In: Proceedings of the 5th Annual IEEE System, Man, and Cybernetics Information Assurance Workshop, pp. 424-431
17. Tandon, G., Chan, P., Mitra, D., 2004. MORPHEUS: Motif oriented representations to purge hostile events from unlabeled sequences. In: Proceedings of the 2004 ACM Workshop on Visualization and Data Mining for Computer Security, pp. 16-25
18. Wagner, D., Soto, P., 2002. Mimicry attacks on host-based intrusion detection systems. In: Proceedings of the 9th ACM Conference on Computer and Communications Security, pp. 255-264
19. Wagner R.A., and Fischer M.J. The string-to-string correction problem. Journal of the ACM 21 1 (1974) 168-173
20. Wang, K., Stolfo, S.J., 2003. One-class training for masquerade detection. In: Proceedings of the 3rd IEEE International Conference on Data Mining Workshop on Data Mining for Security Applications
21. Wespi, A., Dacier, M., Debar, H., 1999. An intrusion-detection system based on the teiresias pattern-discovery algorithm. In: EICAR 1999 Best Paper Proceedings, pp. 1-15
22. Wright, C., Monrose, F., Masson, G.M., 2004. HMM profiles for network traffic classification. In: Proceedings of the 2004 ACM Workshop on Visualization and Data Mining for Computer Security, pp. 9-15
23. Wright C.V., Monrose F., and Masson G.M. On inferring application protocol behaviors in encrypted network traffic. Journal of Machine Learning Research 7 (2006) 2745-2769
24. Zhang Z., Schwartz S., Wagner L., and Miller W. A greedy algorithm for aligning DNA sequences. Journal of Computational Biology 7 1-2 (2000) 203-214