Abstract
Inspired by the immunity theory, a new immune-based dynamic intrusion response model, referred to as IDIR, is presented. An intrusion detection mechanism based on self-tolerance, clone selection, and immune surveillance, is established. The method, which uses antibody concentration to quantitatively describe the degree of intrusion danger, is demonstrated. And quantitative calculations of response cost and benefit are achieved. Then, the response decision-making mechanism of maximum response benefit is developed, and a dynamic intrusion response system which is self-adaptation is set up. The experiment results show that the proposed model is a good solution to intrusion response in the network.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fisch, E.A.: Intrusion Damage Control and Assessment: A Taxonomy and Implementation of Automated Responses to Intrusive Behavior. Ph.D. Dissertation, Texas A&M University, College Station TX (1996)
Carver, C.A., Pooch, U.W.: An Intrusion Response Taxonomy and its Role in Automatic Intrusion Response. In: Proceedings of the 2000 IEEE Workshop on Information Assurance and Security, pp. 129–135. West Point, New York (2000)
Toth, T.: Evaluating the Impact of Automated Intrusion Response Mechanisms. In: 18th Annual Computer Security Applications Conference (ACSAC 2002) (2002)
Forrest, S., Perelson, A., Cherukuri, R.: Self-Nonself Discrimination in a Computer. In: Proceedings of IEEE Symposium on Research in Security and Privacy, Oakland (1994)
Kim, J., Bentley, P.J.: Immune Memory in the Dynamic Clonal Selection Algorithm. In: 1st International Conference on Artificial Immune Systems (ICARIS-2002), September 2002, University of Kent at Canterbury, UK (2002)
Lee, W., Fan, W., Miller, M.: Toward Cost-sensitive Modeling for Intrusion Detection and Response [C]. In: 1st ACM Workshop on Intrusion Detection Systems (2000)
Chao, D.L., Davenport, M.P., Forrest, S., Perelson, A.: A Stochastic Model of Cytotoxic Tcell Responses. Journal of Theoretical Biology 228(2), 227–240 (2004)
Varela, F.J., Stewart, J.: Dynamic of a Class of Immune Network. Global Stability of Idiotype Interactions. J. Theoretical Biology (144), 93–101 (1990)
Li, T.: An immune based dynamic intrusion detection model. Chinese Science Bulletin 50, 2650–2657 (2005)
Li, T.: An immunity based network security risk estimation. Science in China Ser. F. Information Sciences. 48, 557–578 (2005)
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
Liu, S., Li, T., Zhao, K., Yang, J., Gong, X., Zhang, J. (2006). Immune-Based Dynamic Intrusion Response Model. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_13
Download citation
DOI: https://doi.org/10.1007/11903697_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
eBook Packages: Computer ScienceComputer Science (R0)