MOVICAB-IDS: Visual analysis of network traffic data streams for intrusion detection

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

Volumn 4224 LNCS, Issue , 2006, Pages 1424-1433

  Herrero, Álvaro ^a   Corchado, Emilio ^a   Sáiz, José Manuel ^a  

^a UNIVERSITY OF BURGOS   (Spain)

Author keywords

Computer network security; Exploratory projection pursuit; Intrusion detection; Multiagent systems; Neural networks; Unsupervised learning

Indexed keywords

COMPUTER NETWORKS; LEARNING SYSTEMS; MULTI AGENT SYSTEMS; NEURAL NETWORKS; PERSONAL DIGITAL ASSISTANTS; SOFTWARE AGENTS; TELECOMMUNICATION NETWORKS; TELECOMMUNICATION TRAFFIC;

COMPUTER NETWORK SECURITY; DATA STREAMS; INTRUSION DETECTION; NETWORK TRAFFIC; UNSUPERVISED LEARNING; VISUAL ANALYSIS;

DATA STRUCTURES;

EID: 33750561462     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11875581_169     Document Type: Conference Paper

References (23)

1. Abraham, A., Grosan, C., Martin-Vide, C.: Evolutionary Design of Intrusion Detection Programs. International Journal of Network Security (2006)
2. Julisch, K.: Data Mining for Intrusion Detection: A Critical Review. Research Report RZ 3398, IBM Zurich Research Laboratory. Switzerland (2002)
3. Lee, W., Stolfo, S.J.: A Framework for Constructing Features and Models for Intrusion Detection Systems. ACM Transactions on Information and System Security (TISSEC), Vol. 3(4). ACM Press, New York (2000) 227 - 261
4. Herrero, A., Corchado, E., Sáiz, J.M.: A Cooperative Unsupervised Connectionist Model Applied to Identify Anomalous Massive SNMP Data Sending. Proceedings of the International Conference on Natural Computation (ICNC). Lecture Notes in Computer Science, Vol. 3610. Springer-Verlag, Berlin Heidelberg New York (2005) 778-782
5. Corchado, E., Herrero, A., Sáiz J.M.: Detecting Compounded Anomalous SNMP Situations Using Unsupervised Pattern Recognition. Proceedings of the International Conference on Artificial Neural Networks (ICANN 2005). Lecture Notes in Computer Science, Vol. 3697. Springer-Verlag, Berlin Heidelberg New York (2005) 905-910
6. Zanero, S., Savaresi, S.M.: Unsupervised Learning Techniques for an Intrusion Detection System. Proceedings of the ACM Symposium on Applied Computing (2004) 412-419
7. Marchette, D.J.: Computer Intrusion Detection and Network Monitoring: A Statistical Viewpoint. Information Science and Statistics. Springer-Verlag, Berlin Heidelberg New York (2001)
8. Roesch, M.: Snort - Lightweight Intrusion Detection for Networks. Proceedings of the 13th Systems Administration Conference (LISA '99) (1999)
9. Goldring, T.: Scatter (and Other) Plots for Visualizing User Profiling Data and Network Traffic. Proceedings of the ACM Workshop on Visualization and Data Mining for Computer Security (2004)
10. Muelder, Ch., Ma, K-L., Bartoletti, T.: Interactive Visualization for Network and Port Scan Detection. Proceedings of the 8th International Symposium on Recent Advances in Intrusion Detection (RAID). Lecture Notes in Computer Science, Vol. 3858. Springer-Verlag, Berlin Heidelberg New York (2005)
11. Abdullah, K., Lee, Ch., Conti, G., Copeland, J.A.: Visualizing Network Data for Intrusion Detection. Proceedings of the IEEE Workshop on Information Assurance and Security (2002) 100-108
12. MRTG: The Multi Router Traffic Grapher, http://people.ee.ethz.ch/ ~oetiker/webtools/mrtg/
13. Wooldridge, M.: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. Gerhard Weiss (1999)
14. Corchado, E., Han, Y., Fyfe, C.: Structuring Global Responses of Local Filters Using Lateral Connections. Journal of Experimental and Theoretical Artificial Intelligence, Vol. 15(4) (2003) 473-487
15. Corchado, E., Fyfe, C.: Connectionist Techniques for the Identification and Suppression of Interfering Underlying Factors. International Journal of Pattern Recognition and Artificial Intelligence, Vol. 17(8) (2003) 1447-1466
16. Corchado, E., Corchado, J.M., Sáiz, L., Lara, A.: Constructing a Global and Integral Model of Business Management Using a CBR System. Proceedings of the 1st International Conference on Cooperative Design, Visualization and Engineering (CDVE). Lecture Notes in Computer Science, Vol. 3190. Springer-Verlag, Berlin Heidelberg New York (2004) 141-147
17. Friedman J., Tukey. J.: A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transaction on Computers, Vol. 23 (1974) 881-890
18. Hyvärinen A.: Complexity Pursuit: Separating Interesting Components from Time Series. Neural Computation, Vol. 13(4) (2001) 883-898
19. Corchado, E., MacDonald, D., Fyfe, C.: Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit. Data Mining and Knowledge Discovery, Vol. 8(3), Kluwer Academic Publishing (2004) 203-225
20. Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules. Proceedings of the European Symposium on Artificial Neural Networks (2002) 143-148
21. Seung, H.S., Socci, N.D., Lee, D.: The Rectified Gaussian Distribution. Advances in Neural Information Processing Systems, Vol. 10 (1998) 350-356
22. Dreger, H., Feldmann, A., Paxson, V., Sommer, R.: Operational Experiences with High-Volume Network Intrusion Detection. Proceedings of the ACM Conference on Computer and Communications Security. ACM Press, New York, USA. (2004) 2-11
23. Oja, E.: Neural Networks, Principal Components and Subspaces. International Journal of Neural Systems, Vol. 1 (1989) 61-68