Abstract
This research employs unsupervised pattern recognition to approach the thorny issue of detecting anomalous network behavior. It applies a connectionist model to identify user behavior patterns and successfully demonstrates that such models respond well to the demands and dynamic features of the problem. It illustrates the effectiveness of neural networks in the field of Intrusion Detection (ID) by exploiting their strong points: recognition, classification and generalization. Its main novelty lies in its connectionist architecture, which up until the present has never been applied to Intrusion Detection Systems (IDS) and network security. The IDS presented in this research is used to analyse network traffic in order to detect anomalous SNMP (Simple Network Management Protocol) traffic patterns. The results also show that the system is capable of detecting independent and compounded anomalous SNMP situations. It is therefore of great assistance to network administrators in deciding whether such anomalous situations represent real intrusions.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Debar, H., Becker, M., Siboni, D.: A Neural Network Component for an Intrusion Detection System. In: IEEE Symposium on Research in Computer Security and Privacy (1992)
Corchado, E., Herrero, A., Baruque, B., Sáiz, J.M.: Intrusion Detection System Based on a Cooperative Topology Preserving Method. In: International Conference on Adaptive and Natural Computing Algorithms. Springer Computer Science. Springer, NewYork (2005)
Hätönen, K., Höglund, A., Sorvari, A.: A Computer Host-Based User Anomaly Detection System Using the Self-Organizing Map. In: International Joint Conference of Neural Networks (2000)
Zanero, S., Savaresi, S.M.: Unsupervised Learning Techniques for an Intrusion Detection System. In: ACM Symposium on Applied Computing, pp. 412–419 (2004)
Ghosh, A., Schwartzbard, A., Schatz, A.: Learning Program Behavior Profiles for Intrusion Detection. In: Workshop on Intrusion Detection and Network Monitoring (1999)
Friedman, J., Tukey, J.: A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transaction on Computers 23, 881–890 (1974)
Hyvärinen, A.: Complexity Pursuit: Separating Interesting Components from Time Series. Neural Computation 13, 883–898 (2001)
Corchado, E., MacDonald, D., Fyfe, C.: Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit. In: Data Mining and Knowledge Discovery, vol. 8(3), pp. 203–225. Kluwer Academic Publishing, Dordrecht (2004)
Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules. In: European Symposium on Artificial Neural Networks (2002)
Corchado, E., Han, Y., Fyfe, C.: Structuring Global Responses of Local Filters using Lateral Connections. Journal of Experimental and Theoretical Artificial Intelligence 15(4), 473–487 (2003)
Corchado, E., Fyfe, C.: Connectionist Techniques for the Identification and Suppression of Interfering Underlying Factors. International Journal of Pattern Recognition and Artificial Intelligence 17(8), 1447–1466 (2003)
Corchado, E., Corchado, J.M., Sáiz, L., Lara, A.: Constructing a Global and Integral Model of Business Management Using a CBR System. In: 1st International Conference on Cooperative Design, Visualization and Engineering (2004)
Herrero, A., Corchado, E., Sáiz, J.M.: A Cooperative Unsupervised Connectionist Model Applied to Identify Anomalous Massive SNMP Data Sending. In: 1st International Conference on Natural Computation (2005) (in press)
Herrero, A., Corchado, E., Sáiz, J.M.: Identification of Anomalous SNMP Situations Using a Cooperative Connectionist Exploratory Projection Pursuit Model. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 187–194. Springer, Heidelberg (2005) (in press)
Seung, H.S., Socci, N.D., Lee, D.: The Rectified Gaussian Distribution. Advances in Neural Information Processing Systems 10, 350–356 (1998)
Myerson, J.M.: Identifying Enterprise Network Vulnerabilities. International Journal of Network Management 12 (2002)
Cisco Secure Consulting: Vulnerability Statistics Report (2000)
Case, J., Fedor, M.S., Schoffstall, M.L., Davin, C.: Simple Network Management (SNMP). RFC-1157 (1990)
Oja, E.: Neural Networks, Principal Components and Subspaces. International Journal of Neural Systems 1, 61–68 (1989)
Aldwairi, M., Conte, T., Franzon, P.: Configurable string matching hardware for speeding up intrusion detection. In: ACM SIGARCH Computer Architecture News, vol. 33(1) (2005)
Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure, 1st edn. Morgan Kaufmann Publishers, San Francisco (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Corchado, E., Herrero, Á., Sáiz, J.M. (2005). Detecting Compounded Anomalous SNMP Situations Using Cooperative Unsupervised Pattern Recognition. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_143
Download citation
DOI: https://doi.org/10.1007/11550907_143
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
Online ISBN: 978-3-540-28756-8
eBook Packages: Computer ScienceComputer Science (R0)