Abstract
This paper presents an application of a new type of fuzzy inference system, denoted as evolvable-neural-based fuzzy inference system (EFIS), for adaptive network anomaly detection in the presence of a concept drift problem. This problem cannot be avoided to happen in every network. It is a problem of modeling the behavior of normal traffic while it keeps changing over time in continuous manner. EFIS can solve the concept drift problem by having dynamic network traffic profile creation and adaptation. The profile is then being further used to detect anomaly. An enhanced evolving clustering method (ECMm), which is employed by EFIS for online network traffic clustering, is also presented. It is demonstrated, through experiments, that EFIS can evolve in a growing network and also successfully detect network traffic anomalies.
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
Marchette, D.: A Statistical Method for Profiling Network Traffic. In: Workshop on Intrusion Detection and Network Monitor, pp. 119–128 (1999)
Pasha, M.F., Budiarto, R., Sumari, P., Osman, A.: Data Mining and Rule Generation in Network Traffic using Fuzzy Clustering Techniques. In: MMU International Symposium on Information and Communications Technologies, vol. TS4B-5, pp. 17–20 (2004)
Budiarto, R., Pasha, M.F.: Developing Online Adaptive Engine for Profiling Network Traffic using Evolving Connectionist Systems. In: Conference on Neuro-Computing and Evolving Intelligence, pp. 69–70 (2004)
Lampinen, T., Koivisto, H., Honkanen, T.: Profiling Network Application with Fuzzy C-Means Clustering and Self Organizing Map. In: First International Conference on Fuzzy System and Knowledge Discovery: Computational Intelligence for the E-Age, pp. 300–304 (2002)
Kasabov, N.: Evolving Connectionist System: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines. Springer, Heidelberg (2002)
Teng, H.S., Chen, K., Lu, S.C.: Adaptive real-time anomaly detection using inductively generated sequential patterns. In: IEEE Symposium on Security and Privacy, pp. 278–284 (1980)
Lane, T., Brodley, C.: Approaches to online learning and conceptual drift for user identification in computer security. ECE and the COAST Laboratory Tech. Rep. (Coast TR 98-12). Purdue University (1998)
Cannady, J.: Next generation intrusion detection: Autonomous reinforcement learning of network attacks. In: 23rd National Information Systems Security Conference, pp. 1–12 (2000)
Hossain, M., Bridges, S.M.: A framework for an adaptive intrusion detection system with data mining. In: 13th Annual Canadian Information Technology Security Symposium (2001)
Kim, J., Kasabov, N.: HyFIS: Adaptive Neuro-Fuzzy System and Their Application to Non-Linear Dynamical Systems. Neural Network 12(9), 1301–1319 (1999)
Barbara, D., Couto, J., Jajodia, S., Popyack, L., Wu, N.: ADAM: Detecting intrusions by data mining. In: IEEE Workshop on Information Assurance and Security, pp. 11–16 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pasha, M.F., Budiarto, R., Syukur, M., Yamada, M. (2006). EFIS: Evolvable-Neural-Based Fuzzy Inference System and Its Application for Adaptive Network Anomaly Detection. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_69
Download citation
DOI: https://doi.org/10.1007/11739685_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
eBook Packages: Computer ScienceComputer Science (R0)