Computer network monitoring and abnormal event detection using graph matching and multidimensional scaling

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

Volumn 4065 LNAI, Issue , 2006, Pages 576-590

  Bunke, H ^a   Dickinson, P ^b   Humm, A ^a,c   Irniger, Ch ^a   Kraetzl, M ^b  

^a UNIVERSITY OF BERN   (Switzerland)

^b DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION   (Australia)

^c UNIVERSITY OF FRIBOURG   (Switzerland)

Author keywords

Abnormal event detection; Computer network monitoring; Graph matching, multidimensional scaling.

Indexed keywords

COMPUTER GRAPHICS; DATA PROCESSING; GRAPH THEORY; RESEARCH AND DEVELOPMENT MANAGEMENT; TIME SERIES ANALYSIS;

ABNORMAL EVENT DETECTION; COMPUTER NETWORK MONITORING; GRAPH MATCHING, MULTIDIMENSIONAL SCALING; TWO-DIMENSIONAL PLANES;

COMPUTER NETWORKS;

EID: 33746452169     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11790853_45     Document Type: Conference Paper

References (24)

1. Kruegel, C. and Toth, T: Using decision trees to improve signature-based intrusion detection. RAID, 2003.
2. Mahoney, M. and Chan, P.: Learning rules for anomaly detection of hostile network traffic. In ICDM 2003: Third IEEE International Conference on Data Mining, pages 601-604, Washington, DC, USA, 2003. IEEE Computer Society.
3. Lewis, L.: A case based reasoning approach to the managment of faults in communications networks. In IEEE INFOCOM, volume 3, pages 1422-1429, San Francisco, CA, March 1993.
4. Bon, K. S.: Signature-Based Approach for Intrusion Detection. In MLDM 2005: 4th International Conference, pages 526-536, Leipzig, Germany, 2005.
5. Lazar, A., Wang, W. and Deng, R.: Models and algorithms for network fault detection and identification: A review. In ICC, Singapore, November 1992.
6. Barford, P. and Plonka, D.: Characteristics of network traffic flow anomalies. In IMW '01: Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement, pages 69-73, San Francisco, California, USA, 2001. ACM Press.
7. Thottan, M. and Ji, C.: Proactive anomaly detection using distributed intelligent agents. IEEE Network, 12(5):21-27, September 1998.
8. Cabrera, J.B.D., Lewis, L., Qin, X., Lee, W., Prasanth, R.K., Ravichandran B., and Mehra, R.K.: Proactive detection of distributed denial of service attacks using mib traffic variables - a feasibility study. In 2001 IEEE/IFIP International Symposium on Integrated Network Management Proceedings, pages 609-622, May 2001.
9. Hellerstein, J. and Watson, T..I.: An approach to selecting metrics for detecting performance problems in information systems. Proceedings of Second IEEE International Workshop on Systems Management, pages 30-39, 1996.
10. Hood, C.S. and Ji, C.: Intelligent network monitoring. In Proceedings of the 1995 IEEE Workshop on Neural Networks for Signal Processing, pages 521-530, 1995.
11. Magnaghi, A., Hamada, T., and Katsuyama, T.: A wavelet-based framework for proactive detection of network misconfigurations. In SIGCOMM 2004, pages 253-258, August 2004.
12. Hood, C.S. and Proactive, C.Ji.: Network-fault detection. IEEE Trans. Reliability, 46(3):333-341, 1997.
13. Giacinto, G. and Perdisci, R. and Roll, F.: Alarm Clustering for Intrusion Detection Systems in Computer Networks. In MLDM 2005: 4th International Conference, pages 184-193, Leipzig, Germany, 2005.
14. Bunke, H., Kraetzl, M., Shoubridge, P., Wallis, W.D.: Detection of abnormal change in time series of graphs, Journal of Interconnection Networks, Vol.3, Nos 1&2, 2002, 85-101
15. Dickinson, P., Bunke, H., Dadej, A., Kraetzl, M.: Median graphs and anomalous change detection in communication networks, Proc. Int. Conference on Information, Decision and Control, Adelaide, 2002, 59-64
16. Bunke, H., Kraetzl, M.: Classification and detection of abnormal events in time series of graphs, in Last M., Kandel, A., Bunke, H. (Eds.): Data Mining in Time Series Databases, World Scientific, 2004, 127-148
17. Sanfeliu, A., Fu, K.S.: A distance measure between attributed relational graphs for pattern recognition, IEEE Trans. SMC, 13, 1983, 353-363
18. Dickinson, P., Bunke, H., Dadej, A., Kraetzl, M.: Matching graphs with unique node labels, Pattern Analysis and Applications 7(3), 2004, 243-254
19. Cox, T.F. and Cox, M.A.A.: Multidimensional Scaling. Chapman & Hall, 1995
20. Borg, I., Groenen, P.: Modern Multidimensional Scaling, Springer, 1997
21. Ullman, J.: An Algorithm for subgraph isomorphism, Journal of the Association for Computing Machinery, 23(1), 1976, 31-42
22. McGregor: Backtrack search algorithms and the maximal common subgraph problem, Software-Practice and Experience, 12, 1982, 23-13
23. Jain, A., Murty, M., Flynn, P.: Data clustering: a review. ACM Computing Surveys 31 (1999) 264-323
24. Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, Knowledge Discovery and Data Mining, (1996) 226-231