Abstract
There have been many researches in Computer Science that their fundamental ideas were based on Biology. Genetic algorithm and neural network are best-known paradigms in this category. Recently, many ideas from immune system have been used in detecting computer virus and worm. Since the first computer virus has been found, scanning detection has been used as a primarily method in virus detection systems. As computer viruses and worms become more complex and sophisticated, the scanning detection method is no longer able to detect various forms of viruses and worms effectively. Many anti-virus researchers proposed various detection methods including artificial immune system to cope with these characteristics of computer viruses and worms. This paper discusses the principle of artificial immune system and proposes artificial immune based virus detection system that can detect unknown viruses.
This work was supported by grant No. (R05-2003-000-11235-0) from the Basic Research Program of the Korea Science & Engineering Foundation
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References
Spafford, E.H.: Computer Viruses as Artificial Life. In: Artificial Life II, Addison-Wesley, Reading (1992)
Marko, R.: Heuristics: Retrospective and Future. In: Virus Bulletin Conference 2002 (2002)
Malivanchuk, T.: The WIN32 worms: Classification and Possibility of Heuristic detection. In: Virus Bulletin Conference 2002 (2002)
Bonchev, V.: Macro and Script Virus Polymorphism. In: Virus Bulletin Conference (2002)
Szappanos, G.: Are There Any Polymorphic Macro Viruses at All? In: Virus Bulletin Conference (2002)
Forrest, S.: Self-Nonself Discrimination in a Computer. In: IEEE Symposium on Research in Security and Privacy 1994 (1994)
Anderson, J.P.: Computer Security threat monitoring and surveillance, Technical report (1980)
Denning, D.E.: An Intrusion-Detection Model. IEEE, Los Alamitos (1986)
Wagner, D., Dean, D.: Intrusion Detection via Static Analysis. IEEE, Los Alamitos (2001)
Forrest, S., Hofmeyr, S., Somayaji, A., Longstaff, T.: A Sense of Self for Unix Processes. IEEE, Los Alamitos (1996)
Kephart, J.O.: A Biologically Inspired Immune System for Computers. In: High Integrity Computing Laboratory IBM Thomas J. Watson. Artificial Life IV: Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems 1994 (1994)
Kim, J., Bentley, P.: The Human Immune System and Network Intrusion Detection. Submitted to EUFIT 1999 (1999)
Digging For Worms, Fishing For Answers (2002)
Dasgupta, D., Attoh-Okine, N.: Immunity-Based Systems: A survey. In: IEEE International Conference 1997 (1997)
Somayaji, A., Hofmeyr, S., Forrest, S.: Principles of a Computer Immune System (1997)
Marmelstein, R.E., Van Veldhuizen, D.A., Lamont, G.B.: A Distributed Architecture for an Adaptive CVIS
The Digital Immune System, Symantec technical report
Forrest, S., Somayaji, A., Ackley, D.H.: Building Diverse Computer Systems. IEEE, Los Alamitos (1997)
Dasgupta, D., Forrest, S.: Novelty Detection in Time Series Data Using Ideas from Immunology. In: International Conference on Intelligent System (1999)
Dasgupta, D.: Artificial Neural Networks and Artificial Immune System: Similarities and Differences. IEEE, Los Alamitos (1997)
Okamoto, T., Ishida, Y.: Multiagent Approach Computer virus: An Immunity-Based System. In: AROB 1999 (1999)
Williams, P.D., Anchor, K.P., Bebo, J.L., Gunsch, G.H., Lamont, G.B.: CDIS: Toward a computer immune system for detecting network intrusions. In: Proc. Fourth Int. Symp. Recent Advances in Intrusion Detection, October 2001, pp. 117–133 (2001)
de Castro, L.N., Von Zuben, F.J.: Artificial Immune Systems: Part I – Basic Theory and Applications. Technical report, TR-DCA 01/99
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Lee, H., Kim, W., Hong, M. (2004). Biologically Inspired Computer Virus Detection System. In: Ijspeert, A.J., Murata, M., Wakamiya, N. (eds) Biologically Inspired Approaches to Advanced Information Technology. BioADIT 2004. Lecture Notes in Computer Science, vol 3141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27835-1_12
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DOI: https://doi.org/10.1007/978-3-540-27835-1_12
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