Scientific and Technical Journal

ELECTROTECHNIC AND COMPUTER SYSTEMS

ISSN Print 2221-3937
ISSN Online 2221-3805
AN ADAPTIVE MODEL OF INTRUSION DETECTION IN COMPUTER NETWORKS BASED ON ARTIFICIAL IMMUNE SYSTEM
Abstract:

It is proposed adaptive model and it is based  the intrusion detection system (IDS), which is constructed on the basis of immunological principles. Recognition of the state of network traffic is  in conditions of shortage  priori information about the properties of the source intrusion and the stochastic nature of recognizable events. In order to improve the reliability of intrusion detection system is made adaptive setting decision rules for classifying the states of network traffic. The system is designed for the detection and classification of network attacks classes: DoS, R2L, U2R, Probе. Setting up and testing of the model is based on the search  of anomalies in real data sets of IP-traffic computer networks and contained in known database KDD'99.

Authors:
Keywords
DOI
References
  1. Dasgupta D. (ed.) Iskusstvennye immunnye sistemy i ikh primenenie [Artificial Immune Systems and Applications], (2006), Moscow, Russian Federation, Pod red. D. Dasgupty, Fizmatlit Publ., 344 p. (In Russian).
  2. Skatkov A.V. (ed.) Informatsionnye tekhnologii dlya kriticheskikh infrastruktur: monografiya [Information Technology for Critical Infrastructures: Monograph.], (2012), Pod red. A.V. Skatkova, Sevastopol, Ukraine, SevNTU, 306 p. (In Russian).
  3. Varghese S.M., and Jacob K.P. Anomaly Detection Using System Call Sequence Sets, (2007), Journal of Software Publ.,pp.14 – 21.
  4. Yeung D.Y., and Ding Y. Host-Based Intrusion Detection Using Dynamic and Static Behavioral Models, (2003), Journal of Pattern Recognition Publ., pp.229 – 243.
  5. Ji Z., and Dasgupta D. Real-Valued Negative Selection Algorithm with Variable-sized Detectors, (2004), Proceedings of the Genetic and Evolutionary Computation, Seattle, WA, USA, pp. 287 – 298.
  6. Chen Y., Abraham A., and Yang B. Hybrid Flexible Neural-Tree-Based Intrusion Detection Systems, (2007), International Journal of Intelligent Systems Publ., pp. 337 –352.
  7. Shon T., and Moon J.A. Hybrid Machine Learning Approach to Network Anomaly Detection, (2007), Journal of Information Sciences Publ., pp. 3799 – 3821.
  8. Kabiri P., and Ghorbani A. Research in Intrusion Detection and Response,(2005), International Journal of Network Security Publ., pp. 84 – 102.
  9. Beghdad R. Critical Study of Neural Networks in Detecting Intrusions, (2008), Journal of Computers and Security Publ., pp.168 – 175.
  10. Castro P.A., Coelho, G.P., and Von Zuben F.J. Designing Ensembles of Fuzzy Classification Systems: An Immune-Inspired Approach, (2005), Paper presented at the 4th International Conference on Artificial Immune Systems (ICARIS), Springer–Verlag, Berlin, pp. 469 – 482.
  11. Bryukhovetskyi A.A. Skatkov A.V., and Berezenko P.O. Obnaruzhenie uyazvimostei v kriticheskikh prilozheniyakh na osnove reshayushchikh derev'ev [The Discovery of Vulnerabilities in Critical Applications Based on Decision Trees], (2013), Journal Electronic and Computer Systems Publ., Kharkov, Ukraine, Vol. 5(64),   pp. 18 – 23.
  12. Rutkovska D. Neironnye seti, geneticheskie algoritmy i nechetkie siste-my [Neural Networks, the Genetic Algorithms and Fuzzy Systems], (2006), Moscow, Russian Federation,Goryachaya liniya Publ., 452 p.
  13. Abraham A., and Jain R. Soft Computing Models for Network Intrusion Detection Systems. Classification and Clustering for Knowledge Discovery Studies, (2005), Journal Computational Intelligence Publ., pp. 191 – 207.
  14. Roit I., Brostoff Dzh., and  Meil D. Immunologya] [Immunology], (2000), Per. s angl., Moscow, Russian Federation,Mir Publ., 592 p. (In Russian).
  15. KDD cup 99 Intrusion  Detection Data Set [Elektronnyi Resurs], Electron. Textsdata (752 Мб),Darpa: Irvine, CA 92697-3425, (1999), available at: /http: // kdd.ics.uci.edu /databases/kddcup99/ accessed: Monday, 17 March 2013, 19:07:34.  
Published:
Last download:
2017-11-18 04:14:54

[ © KarelWintersky ] [ All articles ] [ All authors ]
[ © Odessa National Polytechnic University, 2014. Any use of information from the site is possible only under the condition that the source link! ]
Яндекс.Метрика