Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Intrusion Detection System for MANET using Soft-Computing

Author : Namita Parati 1 Sumalatha Poteti 2

Date of Publication :7th February 2017

Abstract: Due to the advancement in wireless technologies, many of new paradigms have opened for communications. Among these technologies, mobile ad hoc networks play a prominent role for providing communication in many areas because of its independent nature of predefined infrastructure. But in terms of security, these networks are more vulnerable than the conventional networks because firewall and gateway based security mechanisms cannot be applied on it. That’s why intrusion detection systems are used as keystone in these networks. Many number of intrusion detection systems have been discovered to handle the uncertain activity in mobile ad hoc networks. This paper proposed a novel intrusion detection system based on soft computing techniques for mobile ad hoc networks. The proposed system is based on neuro-fuzzy classifier in binary form to detect, one of vey possible attack, i.e. packet dropping attack in mobile ad hoc networks

Reference :

    1. Y. Li and J. Wei., “Guidelines on selecting intrusion detection methods in MANET”, In Proceedings of the Information Systems Educators Conference, 2004.
    2. A. Hasti, “ Study o f I m p a c t o f M o b i l e AdHoc Networking and its Future Applications”, BIJIT – 2012; January - June, 2012; Vol. 4 No. 1; ISSN 0973 – 5658.
    3. Y. Zhang and W. Lee., “ Intrusion detection in wireless ad hoc networks” , In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking (MobiCom'00), pages 275-283, 2000.
    4. IETF Mobile Ad-Hoc Networks Working Group (MANET), IETF
    5. R. Heady, G. Luger, A. Maccabe, and M. Servilla, “The architecture of a network level intrusion detection system” Technical report, Computer Science Department, University of New Mexico, August 1990.
    6. S. Kumar and E. H. Spafford, “A software architecture to support misuse intrusion detection” In Proceedings of the 18th national Information Security Conference, pages 194- 204, 1995.
    7. K. Ilgun, R. A. Kemmerer, and P.A. Porras, “State transition Analysis: A rule- based intrusion detection approach”, IEEE Transactions on software Engineering, Vol. 21 No. 3:181-199, March 1995.
    8. T.Lunt, A. Tamaru, F. Gilham, R. Jagannathan, P.Neumann, H. Javitz, A. Valdes, and T.Garvey, “A real time intrusion detection expert system (IDES) final technical report.
    9. Uppuluri P, Sekar R, “Experiences with Specification- based Intrusion Detection”, In Proc of the 4th Int Symp on Recent Adv in Intrusion Detection , pp. 172-189.
    10. S. Sen, J . A . Clark - G u i d e t o W i r e l e s s A d H o c Networks; In: Chapter 17-Intrusion Detection in Mobile Ad Hoc Networks-Springer, 2008
    11. P. Brutch and C. Ko, “Challenges in Intrusion Detection for Wireless Ad-hoc Networks," In Proceedings of 2003 Symposium on Applications and the Internet Workshop, pp. 368-373, January 2003.
    12. B. Shanmugam and N. B. Idris, “Anomaly Intrusion Detection based on Fuzzy Logic and Data Mining”, In Proceedings of the Postgraduate Annual Research Seminar, Malaysia 2006.
    13. M. Wahengbam and N. Marchang, “Intrusion detection in manet using fuzzy logic”, 3rd IEEE National Conference on Emerging Trends and Applications in Computer S c i e n c e ( NCETACS), I S B N : 978-1-4577-0749-0, pp. 189 – 192, Shillong, 30-31 March 2012.
    14. Verma, A. K., R. Anil, and Om Prakash Jain. "Fuzzy Logic Based Revised Defect Rating for Software Lifecycle Performance Prediction Using GMR."Bharati Vidyapeeth’s Institute of Computer Applications and Management, 2009.
    15. J. S. R. Jang, C. T. Sun and E. Mizutani – NeuroFuzzy and Soft Computing - A computational Approach to Learning and Machine Intelligence; First Edition; Prentice Hall of India, 1997.
    16. Watkins, Damian. "Tactical manet attack detection based on fuzzy sets using agent communication." In 24th Army Science Conference, Orlando, FL, 2005.
    17. S. Sujatha, P. Vivekanandan, A. Kannan, “Fuzzy logic controller based intrusion handling system for mobile ad hoc networks”, Asian Journal of Information Technology, ISSN: 1682- 3915, pp.175- 182, 2008.
    18. A. Visconti, H. Tahayori, “ A Biologically – Inspired type-2 fuzzy set based algorithm for detecting misbehaving nodes in ad hoc networks” , International Journal for Infonomics, Vol.3, No.2, pp. 270-277, June 2010.
    19. R. Vijayan V. Mareeswari and K. Ramakrishna, “Energy based trust solution for detecting selfish nodes in manet using fuzzy logic”, International Journal of research and reviews in computer science , Vo. 2, No. 3, pp. 647-652, June 2011.
    20. Kulbhushan and Jagpreet Singh, “Fuzzy logic based intrusion detection system against blackhole attack AODV in manet”, IJCA Special issue on “Network Security and Cryptography” Vol. NSC, No. 2 pp. 28-35, December, 2011.
    21. M. Wahengbam and N. Marchang, “Intrusion detection in manet using fuzzy logic”, 3rd IEEE National Conference on Emerging Trends and Applications in Computer S c i e n c e .
    22. W. Lee, S.J. Stolfo, K.W. Mok. “A Data Mining Framework for Building Intrusion Detection Models”. IEEE Symposium on Security and Privacy (Oakland, California), 1999

Recent Article