A Bio-inspired Approach for Self-protecting an Organic Middleware with Artificial Antibodies
Our human body is well protected by antibodies from our biological immune system. This protection system matured over millions of years and has proven its functionality. In our research we are going to transfer some techniques of a biological immune system to a computer based environment. Our goal is to design a self-protecting middleware which is not vulnerable to malicious events. First off this paper proposes an artificial immune system and evaluates optimal parameter settings. This shows the correlation between the size of a system and the length of the receptors used within antibodies for an efficient detection. Our tests showed that the recognition rate of unknown malicious objects can reach up to 99%. Further on we describe the integration of the immune system into our organic middleware OCμ and afterwards we propose techniques to minimize the memory space needed for storing the antibodies and to speedup the time needed for detecting malicious messages. We obtained a space minimization by 30% and gained a speedup of 30 with execution time optimization.
KeywordsRecognition Rate Message Type Incoming Message Recognition Probability Organic Computing
Unable to display preview. Download preview PDF.
- 2.Burgess, M.: Computer Immunology. In: Twelfth Systems Administration Conference (LISA 1998), Boston, Massachusetts (December 1998)Google Scholar
- 4.de Castro, L.N., Timmis, J.: Artificial Immune Systems: A Novel Paradigm to Pattern Recognition. In: Corchado, J.M., Alonso, L., Fyfe, C. (eds.) Artificial Neural Networks in Pattern Recognition, SOCO 2002, University of Paisley, UK, pp. 67–84 (2002)Google Scholar
- 5.de Castro, L.N., von Zuben, F.J.: Biologically Inspired Computing. Idea Group Publishing (2005)Google Scholar
- 6.D’haeseleer, P.: An Immunological Approach to Change Detection: Theoretical Results. In: 9th IEEE Computer Security Foundations Workshop, Dromquinna Manor, County Kerry, Ireland. IEEE, Los Alamitos (1996)Google Scholar
- 7.Hall, J.M., Frincke, D.A.: An Architecture for Intrusion Detection Modeled After the Human Immune System. In: Proceedings of the International Conference on Computer, Communication and Control Technologies, vol. 6, pp. 75–78 (2003)Google Scholar
- 10.Hofmeyr, S.A.: An Immunological Model of Distributed Detection and Its Application to Computer Security. Ph.D thesis, University of New Mexico (May 1999)Google Scholar
- 12.Kephart, J.O., Chess, D.M.: The Vision of Autonomic Computing, pp. 41–50. IEEE Computer Society, Los Alamitos (2003)Google Scholar
- 13.Müller-Schloer, C.: Organic Computing Initiative (April 2004), Published as PDF on: http://www.informatik.uni-augsburg.de/lehrstuehle/sik/research/organiccomputing/download/OC-english.pdf
- 14.Rivest, R.: The MD5 Message-Digest Algorithm. Technical Report Request for Comments: 1321, Internet Engineering Task Force (IETF) (April 1992)Google Scholar