CFP last date
20 January 2025
Reseach Article

An Effective Intrusion Detection System for Routing Attacks in MANET using Machine Learning Technique

by Pratik Gite, Sanjay Thakur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 9
Year of Publication: 2015
Authors: Pratik Gite, Sanjay Thakur
10.5120/19856-1797

Pratik Gite, Sanjay Thakur . An Effective Intrusion Detection System for Routing Attacks in MANET using Machine Learning Technique. International Journal of Computer Applications. 113, 9 ( March 2015), 37-44. DOI=10.5120/19856-1797

@article{ 10.5120/19856-1797,
author = { Pratik Gite, Sanjay Thakur },
title = { An Effective Intrusion Detection System for Routing Attacks in MANET using Machine Learning Technique },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 9 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 37-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number9/19856-1797/ },
doi = { 10.5120/19856-1797 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:50:31.440763+05:30
%A Pratik Gite
%A Sanjay Thakur
%T An Effective Intrusion Detection System for Routing Attacks in MANET using Machine Learning Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 9
%P 37-44
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Wireless communication is widely adopted and application oriented technology There are a huge literature about Mobile Ad-hoc network is available. In these studies, the ad hoc network has two major issues security and performance. In this paper a feasible and adoptable solution is introduced for enhancing security in MANET. The presented work utilizes the network characteristics and their behavioral difference during attack. Using the attack and normal network behavior a machine learning algorithm is trained and the malicious patterns are distinguished according to the new network samples. The proposed machine learning based ad hoc network security is implemented using NS2 simulator and the performance of the system is evaluated in terms of metrics viz. throughput, packet delivery ratio, end to end delay and energy consumption. According to the obtained results the performance of the proposed secure network is optimum and adoptable.

References
  1. Kshmeera N. Khachar and Mrs. Jayna B. Shah, “Detection and Prevention of Black hole Attack in Mobile Ad-hoc Networks: A Survey”, IOSR Journal of Computer Engineering (IOSR-JCE), e-ISSN: 2278-0661, P-ISSN: 2278-8727, Vol. 26, Issue 2, Ver. XI (May-April 2014), PP. 108-112.
  2. Martin K Parmar, Harikrishna B Jethva, “Survey on Mobile ADHOC Network and Security Attacks on Network Layer”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 11, November 2013.
  3. Meghna Chabra and B.B. Gupta, “AN Efficient Scheme to Prevent DDos Flooding Attacks in Mobile Ad-hoc Network (MANET)”, Research Journal of Applied Sciences, Engineering and Technology 7 (10): 2033-2039, 2014, ISSN: 2240-7459; e-ISSN: 2040-7467, PP. 2033-2039, © Maxwell Scientific Organization, 2014.
  4. Asma Tuteja, Rajneesh Gujral, Sunil Thalia, “Comparative Performance Analysis of DSDV, AODV and DSR Routing Protocols in MANET using NS2”, 2010 International Conference on Advances in Computer Engineering, 978-0-7695-4058-0/10 $26.00 © 2010 IEEE.
  5. Jaydip Sen, M. Girish Chandra, Harihara S.G., Harish Reddy, P. Balamurlidhar, “ A Mechanism for Detection of Grayhole Attack in Mobile Ad-hoc Networks”, ICICS 2007, 1-4244-0983-7/07/$25.00©2007 IEEE.
  6. XiaoHang Yao, “A Network Intrusion Detection Approach Combined with Genetic Algorithm and Back Propagation Neural Network”, 2010 International Conference on E-Health Networking, Digital Ecosystem and Technologies, PP. 402-405, 978-1-4244-5517-1/10/$26.00©2010 IEEE.
  7. Abhinav Jain, Sanjay Sharma and Mahendra Singh Sisodiya, “Network Intrusion Detection by using Supervised and Unsupervised Machine Learning Technique: A Survey”, International Journal of Computer Technology and Electronics Engineering (IJCTEE), PP. 14-20, ISSN 2249-6343, Volume 1, Issue3.
  8. Elhadi M. Shakshuki, Nan Kang, and Tarek R. Sheltami, “EAACK—A Secure Intrusion-Detection System for MANETs”, IEEE Transactions on Industrial Electronics, Vol. 60, No. 3, March 2013
  9. Martin K Parmar, Harikrishna B Jethva, “Survey on Mobile ADHOC Network and Security Attacks on Network Layer”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 11, November 2013.
  10. Context-Sensitive and Adaptive Routing in Wireless Mobile Ad-Hoc Networks Using Cross-Layer Design, http://www.uni-marburg.de/fb12/verteilte_systeme/ forschung/pastproj/adhoc_routing_emul
  11. AsmaTuteja, Rajneesh Gujral, Sunil Thalia, “Comparative Performance Analysis of DSDV, AODVand DSR Routing Protocols in MANET using NS2”,2010 International Conference on Advances in Computer Engineering,978-0-7695-4058-0/10 $26.00 © 2010 IEEE.
  12. R. Bronson and G. Naadimuthu, “Operations Research”, 2 ed., Schaum’s Outlines, McGraw Hill, New York, 1997.
  13. Atul Patel, Ruchi Kansara, Dr. Paresh Virparia, “A Novel Architecture for Intrusion Detection in Mobile Ad-hoc Network”, International Journal of Advanced Computer Science and Applications”, PP. 68-71, Special Issue on Wireless and Mobile Ad-hoc Networks.
  14. CH.V. Raghvendram, G. Naga Satish and P. Suresh Varma, “Security Challenges and Attack in Mobile Ad-HOC Networks”, I.J. Information Engineering and Electronics Business, 2013, 3, PP. 49-58.
  15. Weichao Wang, Bharat Bhargava, Yi Lu and Xiaoxin Wu, “Defending Against Wormhole Attacks in Mobile Ad-hoc Networks”, Wiley Journal of Wireless Communication and Mobile Computing (WCMC), PP. 1-24.
  16. Ochola EO and Eloff MM, “A Review of Black Hole Attack on AODV Routing in MANET, Marsan, M.A. (2001): Optimal multicast scheduling in input-queued switches, Proc. IEEE Communications, pp. 2021-2027.
  17. J. Ryan, M. Lin and R. Miikkulainen, Intrusion detection with neural networks. AI approaches to fraud detection and risk management: papers from the 1997 AAAI workshop (Providence, Rhode Island), PP. 72-79.
  18. Z.M. Yang et al., "An intrusion detection system based on RBF neural network", Proceedings of the Ninth International Conference on Computer Supported Cooperative Work in Design, 2005. Vo1.2, 2005, cscwd, PP. 873-875.
  19. J. Zhong, Z.O. Li et al., Intrusion Detection Based on Adaptive RBF Neural Network. Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) - Volume 02, 2006, PP. 1081- 1084.
  20. Z.J. Tang et al., Intrusion Detection. Tsinghua University Press, 2004, Chap.2, PP. 6-8.
  21. P. Innella P, O. McMillan, An introduction to intrusion detection systems. Tetrad Digital Integrity, LLC, last updated December 6, 2001, http://www.ecurityfocus.comlinfocus/ 1520.
  22. C. Stergiou, D. Siganos, Neural Networks. http://www.doc.ic.ac. ukI-ndisurprise 96/journal/vol41 cs 1 1/report.html.
  23. Vikas Makram and Shirish Mohan Dubey, “A General Study of Associations rule mining in Intrusion Detection System”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 2, Issue 1, January 2012.
  24. Qinglei Zhang and Wenying Feng, “Network Intrusion Detection by Support Vector and Ant Colony”, In: Proc. of 2009 Intl. workshop on information security and applications (IWISA 2009).
  25. Ashan Ozkaya and Bekir Karlık, “Protocol Type Based Intrusion Detection Using RBF Neural Network” International Journal of Artificial Intelligence and Expert Systems (IJAE), Volume (3): Issue (4), 2012.
Index Terms

Computer Science
Information Sciences

Keywords

MANET NS-2 Packet Delivery Ratio Routing Overhead End to End Delay Energy Consumption Machine Learning Technique