Efficient modeling of discrete events for anomaly detection using hidden Markov models

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3650 LNCS, Issue , 2005, Pages 506-514

  Florez Larrahondo, German ^a   Bridges, Susan M ^a   Vaughn, Rayford ^a  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; DATA ACQUISITION; DISCRETE TIME CONTROL SYSTEMS; FINITE AUTOMATA; INFORMATION RETRIEVAL SYSTEMS; LEARNING ALGORITHMS; PROBABILITY;

ANOMALY DETECTION SYSTEMS; DISCRETE EVENTS; HIDDEN MARKOV MODELS (HMMS); INCREMENTAL LEARNING;

MARKOV PROCESSES;

EID: 33646006367     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11556992_38     Document Type: Conference Paper

References (15)

1. Warrender, C., Forrest, S., Pearlmutter, B.A.: Detecting intrusions using system calls: Alternative data models. In: Proceedings of the IEEE Symposium on Security and Privacy. (1999) 133-145
2. Florez, G., Liu, Z., Bridges, S., Skjellum, A., Vaughn, R.: Lightweight monitoring of MPI programs in real-time. To Appear in Concurrency and Computation: Practice and Experience (2005)
3. Lane, T.: Hidden Markov Models for human/computer interface modeling, In: Proceedings of the 1999 IJCAI Workshop on Learning About Users. (1999) 35-44
4. Barbara, D., Goel, R., Jajodia, S.: Mining malicious data corruption with Hidden Markov Models. In: Proceedings of the 16th Annual IFIP Working Conference on Data and Application Security, Cambridge, England (2002)
5. Forrest, S., Longstaff, T.A.: A sense of self for UNIX processes. In: Proceedings of the 1996 IEEE Symposium on Security and Privacy, Los Alamitos, CA, IEEE Computer Society Press (1996) 120-128
6. Tan, K., Maxion, R.: Determining the operational limits of an anomaly-based intrusion detector. IEEE Journal on Selected Areas in Communications, Special Issue on Design and Analysis Techniques for Security Assurance 21 (2003) 96-110
7. Siraj, A., Bridges, S., Vaughn, R.: Fuzzy cognitive maps for decision support in an intelligent intrusion detection system. In: International Fuzzy Systems Association/North American Fuzzy Information Processing Society (IFSA/NAFIPS) Conference on Soft Computing, Vancouver, Canada (2001)
8. Florez, G., Liu, Z., Bridges, S., Vaughn, R.: Integrating intelligent anomaly detection agents into distributed monitoring systems. Journal of Network and Computer Applications (2005) To Appear.
9. Rabiner, L.: A tutorial on Hidden Markov Models and selected applications in speech recognition. In: Proceedings of the IEEE. Volume 77 of 2. (1989) 257-286
10. MacDonald, I., Zucchini, W.: Hidden Markov and Other Models for Discrete-valued Time Series. Monographs on Statistics and Applied Probability. Chapman and HALL/CRC (1997)
11. Lane, T.: Machine Learning Techniques for the Computer Security Domain of Anomaly Detection. PhD thesis, Purdue University (2000)
12. Kettnaker, V.M.: Time-dependent HMMs for visual intrusion detection. In: Proceedings of the 2003 Conference on Computer Vision and Pattern Recognition Workshop, Madison, Wisconsin (2003)
13. Stenger, B., Ramesh, V., Paragios, N., F.Coetzee, Buhmann, J.M.: Topology free Hidden Markov Models: Application to background modeling. In: Proceedings of the International Conference on Computer Vision, Vancouver, Canada (2001) 297-301
14. Florez-Larrahondo, G., Bridges, S., Hansen, E.A.: Incremental estimation of discrete hidden markov models based on a new backward procedure. In: 20th National Conference on Artificial Intelligence (AAAI-05), Pittsburgh, Pennsylvania (2005) To Appear.
15. Cunningham, R., Lippmann, R.P., Fried, D.J., Garfikle, S.L.: Evaluating intrusion detection systems without attacking your friends: The 1998 darpa intrusion detection evaluation. Network Intrusion Detection (1999)