Biologically-inspired Complex Adaptive Systems approaches to Network Intrusion Detection

Information Security Technical Report

Volumn 12, Issue 4, 2007, Pages 209-217

  Shafi, Kamran ^a   Abbass, Hussein A ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTER NETWORKS; COMPUTER SYSTEMS; SECURITY SYSTEMS;

COMPUTER INFRASTRUCTURES; COMPUTING SYSTEMS; INEVITABLE COMMODITY;

INTRUSION DETECTION;

EID: 36749010766     PISSN: 13634127     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.istr.2007.09.001     Document Type: Article

References (67)

1. Abbass H.A. Pareto-optimal approaches to neuro-ensemble learning. In: Yaochu J. (Ed). Multi-objective machine learning. Series: studies in computational intelligence (2005), Springer-Verlag 407-428
2. Abbass H.A., Bacardit J., Butz M.V., and Llora X. Online adaptation in learning classifier systems: stream data mining (2004), Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign
3. Aickelin U, Bentley P, Cayzer S, Kim J, McLeod J. Danger theory: the link between AIS and IDS? In: Second International Conference on Artificial Immune Systems (ICARIS), Edinburgh; 2003.
4. Aickelin U., Greensmith J., and Twycross J. Immune system approaches to intrusion detection - a review. LNCS vol. 4 (2004) pp. 316-29 [Springer]
5. Arce I. Why attacking systems is a good idea. IEEE Secur Priv, IEEE Comput Soc 2 (2004) 17-19
6. Association C.C.R. Four grand challenges in trustworthy computing (2003), Computing Research Association
7. Axelrod R.M. Complexity of cooperation: the agent-based models of competition and collaboration (1997), Princeton University Press, Princeton, New Jersey
8. Barabási A.-L. Linked: the new science of networks (2002), Perseus Publishing, Cambridge, MA
9. Barabási A.-L., and Albert R. Emergence of scaling in random networks. Science 286 5439 (1999) 509-512
10. Bryant S., and Wang F. Aspects of adaptive reconfiguration in a scalable intrusion tolerant system. Complexity 9 2 (2003) 74-83
11. Budynek J., Bonabeau E., and Shargel B. Evolving computer intrusion scripts for vulnerability assessment and log analysis. 2005 Conference on genetic and evolutionary computation, Washington DC, USA (2005), ACM Press New York, NY, USA
12. Camp J.S., DeBlois P.B., and Committee E.C.I. Current issues survey report, 2007. EDUCAUSE Quart 30 (2007)
13. Cantu-Paz E., and Kamath C. Inducing oblique decision trees with evolutionary algorithms. IEEE Trans Evol Comput 7 1 (2003) 54-68
14. CERT. CERT advisory CA-2003-20 W32/Blaster worm. Available from: (2003).
15. CERT. CERT/CC statistics 1988-2005. Available from: (2005). [retrieved 19.08.05]
16. CSO. 2006 E-crime watch survey. CSO Magaz (2006)
17. DasGupta D. Artificial immune systems and their applications (1998), Springer-Verlag New York, Inc., Secaucus, NJ, USA
18. Dasgupta D. Immunity-based intrusion detection system: a general framework. 22nd National Information Systems Security Conference (NISSC) (1999), NIST
19. Denning D.E. An intrusion-detection model. IEEE Trans Software Eng (1987)
20. Dooley K. Complex adaptive systems: a nominal definition (1996), Arizona State University
21. Dorigo M., and Di Caro G. The ant colony optimization meta-heuristic. New ideas in optimization (1999), McGraw-Hill Ltd., Maidenhead, UK, England pp. 11-32
22. Dozier G, Brown D, Hurley J, Cain K. Vulnerability analysis of AIS-based intrusion detection systems via genetic and particle swarm red teams. In: IEEE Congress on Evolutionary Computation (CEC); 2004.
23. Erdös P., and Rényi A. On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5 (1960) 17-61
24. Forrest S, Hofmeyr SA, Somayaji A, Longstaff TA. A sense of self for Unix processes. In: 1996 IEEE symposium on security and privacy.
25. Forrest S., Perelson A.S., Allen L., and Cherukuri R. Self-nonself discrimination in a computer. IEEE symposium on research in security and privacy (1994), IEEE Computer Society Press, Los Alamitos, CA
26. Foukia N. IDReAM: intrusion detection and response executed with agent mobility. Engineering self-organising systems vol. 3464 (2005), Springer, Berlin pp. 227-39
27. Foukia N., and Hassas S. Managing computer networks security through self-organization: a complex system perspective. Engineering self-organising systems (2004), Springer, Berlin pp. 124-38
28. Gartner I. New Gartner hype cycle highlights five high impact it security risks (2006), Gartner 2006 Press Releases, London, UK
29. Gordon L.A., Loeb M.P., Lucyshyn W., and Richardson R. 11th Annual CSI/FBI computer crime and security survey (2006), Computer Security Institute (CSI)
30. Hoffmeyer J. The swarming body. Fifth congress of the international association for semiotic studies, Berkeley 1994 (1995), Mouton de Gruyter
31. Hofmeyr S.A. Intrusion detection using sequences of system calls. J Comput Secur 6 3 (1998) 151-180
32. Hofmeyr S.A., and Forrest S. Immunity by design: an artificial immune system. Genetic and Evolutionary Computation Conference (GECCO-1999) (1999), Morgan Kaufmann, San Francisco
33. Hofmeyr S.A., and Forrest S. Architecture for an artificial immune system. Evol Comput 8 4 (2000) 443-473
34. Holland J.H. Adaptation in natural and artificial systems (1992), MIT Press Cambridge, MA, USA
35. Holland J.H. Hidden order: how adaptation builds complexity (1995), Addison Wesley Longman Publishing Co., Inc.
36. Humphries JW, Ragsdale DJ, Carver Jr CA. No silver bullet: inherent limitations of computer. In: Fourth world multiconference on Systemics, Cybernetics, and Informatics (SCI'2000), Orlando, Florida, USA; 2000.
37. Jerne N.K. Towards a network theory of the immune system. Ann Immunol 125 1-2 (1974) 373-389
38. Jong K.A., Spears W.M., and Gordon D.F. Using genetic algorithms for concept learning. Machine Learn 13 2-3 (1993) 161-188
39. Juille H, Pollack JB, 1998. Coevolutionary learning: a case study. In: 15th international conference on machine learning, Madison, Wisconsin.
40. Jungwon K, Bentley PJ. Towards an artificial immune system for network intrusion detection: an investigation of clonal selection with a negative selection operator. In: IEEE Congress on Evolutionary Computation (CEC2001); 2001.
41. Kayacik H.G., Heywood M., and Zincir-Heywood N. On evolving buffer overflow attacks using genetic programming. Eighth annual conference on genetic and evolutionary computation, Seattle, Washington, USA (2006), ACM Press, New York, NY, USA
42. Kennedy J, Eberhart R. Particle swarm optimization. In: IEEE international conference on neural networks, Perth, WA, Australia; 1995.
43. Kewley D.L., and Bouchard J.F. DARPA information assurance program dynamic defense experiment summary. IEEE Trans Syst Man and Cybern A 31 4 (2001) 331-336
44. Kim J, Greensmith J, Twycross J, Aickelin U. Malicious code execution detection and response immune system inspired by the danger theory. In: Adaptive and Resilient Computing Security workshop (ARCS-05); 2005a.
45. Kim J, Wilson W, Aickelin U, McLeod J. Cooperative Automated worm Response and Detection ImmunNe Algorithm (CARDINAL) inspired by T-cell immunity and tolerance. In: Fourth International Conference on Artificial Immune Systems (ICARIS), Banff; 2005b.
46. Kim J, Peter JB. Towards an artificial immune system for network intrusion detection: an investigation of dynamic clone selection [J/OL]. In: IEEE 2002, ISBN: 0-7803-7282-4/02; 2005.
47. Matthew M.W. Resilient infrastructure for network security. Complexity 9 2 (2003) 34-40
48. Michalewicz Z. Genetic algorithms + data structures = evolution programs (1996), Springer
49. Mitchell T.M. Machine learning (1997), McGraw-Hill Higher Education
50. Potter M.A., and De Jong K.A. Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8 1 (2000) 1-29
51. Ramos V., and Abraham A. ANTIDS: Self-organized Ant-based clustering model for Intrusion Detection System. Fourth IEEE international workshop on soft computing as transdisciplinary science and technology, Muroran, Japan (2005), Springer-Verlag, Germany
52. Roesch M. Snort-lightweight intrusion detection for networks. In: Proceedings of USENIX LISA '99. 1999. p.229-38.
53. Rosin CD. Coevolutionary search among adversaries. PhD, Computer Science, San Diego, University of California; 1997.
54. Shacham H., Page M., Pfaff B., Goh E.J., Modadugu N., and Boneh D. On the effectiveness of address-space randomization. 11th ACM conference on computer and communications security, Washington DC, USA (2004), ACM Press, New York, NY, USA
55. Shafi K, Abbass H, Zhu W. An adaptive rule-based intrusion detection architecture. In: The security technology conference, the fifth homeland security summit, Canberra, Australia; 2006.
56. Sidiroglou S, Keromytis AD. A network worm vaccine architecture. In: Twelfth IEEE international Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, WET ICE; 2003.
57. Sinclair C, Pierce L, Matzner S. An application of machine learning to network intrusion detection. In: 15th Annual Computer Security Applications Conference (ACSAC'99) Phoenix, AZ, USA; 1999.
58. Somayaji A., Hofmeyr S., and Forrest S. Principles of a computer immune system. New security paradigms workshop (1997), ACM Press
59. Timmis J. Artificial immune systems: a novel data analysis technique inspired by the immune network theory. PhD, Department of Computer Science, University of Wales; 2001.
60. Tsang CH, Kwong S. Multi-agent intrusion detection system in industrial network using ant colony clustering approach and unsupervised feature extraction. In: IEEE International Conference on Industrial Technology (ICIT 2005); 2005.
61. Wagner D., and Soto P. Mimicry attacks on host-based intrusion detection systems. Nineth ACM conference on computer and communications security, Washington, DC, USA (2002), ACM Press, New York, NY, USA
62. Waldrop M.M. Complexity: the emerging science at the edge of order and chaos (1992), Simon and Schuster, New York
63. Walker B., Holling C.S., Carpenter S.R., and Kinzig A. Resilience, adaptability and transformability in social-ecological systems. Ecol Soc 9 2 (2004) 5
64. Wiegand RP. An analysis of cooperative coevolutionary algorithms. Ph.D. Thesis, George Mason University; 2003.
65. Wilson S.W. Classifier fitness based on accuracy. Evol Comput 3 2 (1995) 149-175
66. Wood B.J., and Duggan R.A. Red teaming of advanced information assurance concepts (1999), Defense Technical Information Center
67. Yao X. Evolutionary artificial neural networks. Int J Neural Syst 4 3 (1993) 203-222