We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Fuzzy Logic Based Intrusion Detection System against Blackhole Attack on AODV in MANET

Published on December 2011 by Kulbhushan, Jagpreet Singh
Network Security and Cryptography
Foundation of Computer Science USA
NSC - Number 2
December 2011
Authors: Kulbhushan, Jagpreet Singh
01937a29-de32-4b0b-9ebe-6a3239f85348

Kulbhushan, Jagpreet Singh . Fuzzy Logic Based Intrusion Detection System against Blackhole Attack on AODV in MANET. Network Security and Cryptography. NSC, 2 (December 2011), 28-35.

@article{
author = { Kulbhushan, Jagpreet Singh },
title = { Fuzzy Logic Based Intrusion Detection System against Blackhole Attack on AODV in MANET },
journal = { Network Security and Cryptography },
issue_date = { December 2011 },
volume = { NSC },
number = { 2 },
month = { December },
year = { 2011 },
issn = 0975-8887,
pages = { 28-35 },
numpages = 8,
url = { /specialissues/nsc/number2/4331-spe024t/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Network Security and Cryptography
%A Kulbhushan
%A Jagpreet Singh
%T Fuzzy Logic Based Intrusion Detection System against Blackhole Attack on AODV in MANET
%J Network Security and Cryptography
%@ 0975-8887
%V NSC
%N 2
%P 28-35
%D 2011
%I International Journal of Computer Applications
Abstract

Security[16] is an essential feature for wired and wireless network[1]. But due to its unique characteristics of MANETs[10], it creates a number of consequential security challenges to network. MANETs are vulnerable to various attacks[2], blackhole[12] is one of the possible attack. In this paper, we represent an intrusion detection[5] system for MANETs against blackhole attack using fuzzy logic[4]. Our system successfully detects the blackhole in the network and this information is passed to other nodes also. We also provide a detailed performance evaluation based on various network parameters. Our results show that the proposed system not only detects the blackhole[12] node, but improves the performance of AODV under the blackhole attack.

References
  1. Andrew S Tanenbaum “ Computer Networks “ Prentice Hall of India, third edition.
  2. Bing Wu, Jianmin Chen, Jie Wu, Mihaela Cardei,” A Survey of Attacks and Countermeasures in Mobile Ad Hoc Networks “ Department of Computer Science and Engineering, Florida Atlantic University
  3. C. Perkins, E Belding-Royer,( July 2003) “Ad hoc On-demand Distance Vector (AODV)” Request For Comments (RFC) 3561.
  4. Fuzzy Logic with Engineering Applications by Timothy J.Ross Mcgraw Hill, Inc.
  5. I. Stamouli, P. G. Argyroudis and H. Tewari, (2005) “Real-time intrusion detection for ad hoc Networks”, Sixth IEEE Intl Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM'05), pp.374-380.
  6. J. Martin Leo Manickam Anna and S.Shanmugavel (2007),” Fuzzy based Trusted Ad hoc On-demand Distance Vector Routing Protocol for MANET “,third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob2007).
  7. Kevin Falland Kannan Varadhan, (April, 2005)”NS-Documentation, http://www.isi.edu/nsnam/ns/ns-documentation.html”.
  8. x M. Hollick, J. Schmitt, C. Seipl and R.Steinmetz,( June 2004) “On the effect of node misbehavior in ad hoc networks”, Proc. Of IEEE Intl Conference on Communications (ICC'04), Paris, pp. 3759-3763.
  9. M. Hollick, J. Schmitt, C.Seipl and R.Steinmetz, ( Feb 2004 ) “The ad hoc on- demand distance vector protocol: an analytical model of the route acquisition process”, Proc. of Second Intl Conference on Wired/Wireless Internet Communications (WWIC'04), Frankfurt, pp. 201-212.
  10. MANET Charter,(1998) available at http://www.ietf.org/html.charters/manet -charter html (1998-11-29).
  11. Payal N. Raj and Prashant B. Swadesh (2009) “DPRAODV: A Dynamic Learning System against Blackhole attack in AODV based MANET “, International Journal of Computer Science, Vol. 2.
  12. R.A. Raja Mahmood, A.I. Khan (2007) “A Survey on Detecting Black Hole Attack in AODV- based Mobile Ad Hoc Networks “,Clayton School of information Technology, Monash UniversityAustralia High Capacity Optical Networks and Enabling Technologies, 2007. HONET 2007. International Symposium on
  13. Satoshi Kurosawa, Hidehisa Nakayama, Nei Kato, Abbas Jamalipour and Yoshiaki Nemoto (Nov. 2007) “Detecting Blackhole Attack on AODV-based Mobile Ad Hoc Networks by Dynamic Learning Method “, International Journal of Network Security, Vol.5, No.3, PP.338–346,
  14. Timothy J. Ross,(I2000)”Fuzzy Logic with Engineering Applications”,McGraw Hill International Editions, International Editions.
  15. Tony Larsson and Nicklas Hedman (1998) “Routing Protocols in Wireless Ad-hoc Networks – A Simulation Study “, Lulea University of Technology , Stockholm
  16. V. Karpijoki,( 2000) “Security in Ad hoc Networks”, In Proceedings of the Helsinki University of Technology, Seminars on Network Security, Helsinki, Finland.
  17. Y.Zhang, W. Lee, and Y. Huang,(September 2003) “Intrusion Detection Techniques for Mobile Wireless Networks,” ACM/Kluwer Wireless Networks Journal (ACM WINET), Vol. 9, No. 5.
  18. Y. Zhang, W. Lee,(August,2000) “Intrusion Detection on Wireless Ad hoc Networks”, in Proceedings 6th Annual International Conference on Mobile Computing and Networking (MobiCom’00).
Index Terms

Computer Science
Information Sciences

Keywords

MANET Blackhole Attack Fuzzy Logic