The week's pick
Random Articles
Reseach Article
Intrusion Detection System for Mobile Ad Hoc Networks using Cross Layer and Machine Learning Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 179 - Number 34 |
| Year of Publication: 2018 |
| Authors: T. Poongothai, K. Jayarajan |
10.5120/ijca2018916680
|
T. Poongothai, K. Jayarajan . Intrusion Detection System for Mobile Ad Hoc Networks using Cross Layer and Machine Learning Approach. International Journal of Computer Applications. 179, 34 ( Apr 2018), 5-12. DOI=10.5120/ijca2018916680
Abstract
Mobile Ad Hoc Networking (MANET) has become a key technology in recent years because of the increased usage of wireless devices and their ability to provide temporary and instant wireless networking in situations like flooding and defense. In spite of their attractive applications, MANET poses high security problems compared to conventional wired and wireless networks due to its unique characteristics such as lack of central coordination, dynamic topology, temporary network life and wireless nature of communication. It is essential to have effective security system to provide trusted communication in MANET. Intrusion detection plays a major role in the security system of Mobile ad hoc networks. Data collected for intrusion detection system contains redundant and irrelevant features. Inclusion of these features result in poor predictions and high computational overhead. Feature selection process finds the most discriminative features that increase the detection accuracy and efficiency of the IDS. This study aims to select the important features using genetic algorithm and enhance the performance of SVM classifier. The performance of the system is validated using Network Simulator (NS2). The experimental results proved that the detection accuracy of detection with all features is 96.37% and genetic feature selection is 98.22%. The results demonstrate that the proposed IDS effectively detect the anomalies with high detection accuracy.
References
- Chih-Fong Tsai, Yu-Feng Hsu , Chia-Ying Lin and Wei-Yang Lin, 2009. Intrusion detection by machine learning: A review Expert Systems with Applications, 36(10),pp.11994-12000.
- Farhan Abdel-Fattah, Zulkhairi Md. Dahalin and Shaidah Jusoh, 2010. Dynamic Intrusion Detection Method for Mobile Ad Hoc Network Using CPDOD Algorithm IJCA Special Issue on MANETs, no .1,pp. 22-29.
- Geethapriya Thamilarasu, Aruna Balasubramanian, Sumita Mishra and Ramalingam Sridhar ,2005.A Cross-Layer Based Intrusion Detection Approach for Wireless Ad Hoc Networks, Proc. IEEE Int’l Conf.Mobile Adhoc and Sensor Systems 2005.
- Joseph, J.F.C., Bu-Sung Lee, Das,A. and Boon-Chong Seet,2011. Cross-Layer Detection of Sinking Behavior in Wireless Ad Hoc Networks Using SVM and FDA IEEE Transactions on Dependable and Secure Computing, vol.8, no.2, pp.233-245.
- LIBSVM -- A Library for Support Vector Machines:www.csie.ntu.edu.tw/~cjlin/libsvm/.
- Liu, Y., Li, Y., and Man, H.,2005. Short Paper: A Distributed Cross-Layer Intrusion Detection System for Ad Hoc Networks Proc. First Int’l Conf. Security and Privacy for Emerging Areas in Comm. Networks 2005 (SecureComm ’05).
- Mishra, A., Nadkarni, K. and Patcha, A., 2004. Intrusion detection in wireless ad hoc networks IEEE Transactions on Wireless Communications, vol. 11, no.1, pp. 48–60.
- Nakayama, H., Kurosawa, S., Jamalipour, A., Nemoto, Y. and Kato, N., 2009. A Dynamic Anomaly Detection Scheme for AODV-Based Mobile Ad Hoc Networks IEEE Transactions on Vehicular Technology, vol.58, no.5, pp.2471-2481.
- Ning,.P and Sun,.K,2003. How to misuse AODV: A case study of insider attacks against mobile ad-hoc routing protocols, In Proceedings of 4th Annual. IEEE Information Assurance Workshop, pp. 60–67.
- Noman Mohammed, Hadi Otrok, Lingyu Wang, Mourad Debbabi and Prabir Bhattacharya ,2011. Mechanism Design-Based Secure Leader Election Model for Intrusion Detection in MANET IEEE Transactions on Dependable and Secure Computing, vol 8, no 1 pp.89-103.
- Ns-2: The Network Simulator, 2010. http://isi.edu/nsnam/ns/.
- Perkins, C., Belding-Royer, E. and Das, S.,2003. Ad hoc On-Demand Distance Vector (AODV) Routing, IETF RFC 3561.
- Rakesh Shrestha, Kyong-Heon Han, Dong-You Choi, Seung-Jo Han, 2010. A Novel Cross Layer Intrusion Detection System in MANET, 24th IEEE International Conference on Advanced Information Networking and Applications, pp.647 – 654.
- Sevil Sen, John A. Clark, 2011. Evolutionary computation techniques for intrusion detection in mobile ad hoc networks, Computer Networks, Vol 55, Issue 15, pp. 3441-3457.
- Sergio Pastrana, Aikaterini Mitrokotsa, Agustin Orfila ,Pedro Peris-Lopez, 2012. Evaluation of classification algorithms for intrusion detection in MANETs Knowledge-Based Systems Vol.36,pp.217-225.
- Shengrong Bu, Richard Yu F., Xiaoping P. Liu, Peter Mason and Helen Tang,2011. Distributed combined authentication and intrusion detection with data fusion in high-security mobile ad hoc networks IEEE Transactions on Vehicular Technology, vol 60 no.3 pp. 1025–1036.
- Khalil El-Khatib Impact of Feature Reduction on the Efficiency of Wireless Intrusion Detection Systems IEEE Transactions on Parallel and Distributed Systems, Vol. 21, no. 8, August 2010.
- Burges,.C.J.C, 1998, A Tutorial on Support Vector Machines for Pattern Recognition Data Mining and Knowledge Discovery, vol. 2, no. 2,pp. 121-167.
- X. Wang, T. L. Lin, and J. Wong, Feature Selection in Intrusion Detection System over Mobile Ad-hoc Network, Technical Report, Computer Science, Iowa State University, USA, 2005.
- G. Stein, B. Chen, A.S. Wu, and K.A. Hua, “Decision Tree Classifier for Network Intrusion Detection with GA-Based Feature Selection,” Proc. 43rd ACM Southeast Regional Conf.—Volume 2, Mar. 2005.
- A.H. Sung and S. Mukkamala, The Feature Selection and Intrusion Detection Problems, Proc. Ninth Asian Computing Science Conf., 2004.
- Sevil Sen, Zeynep Dogmus, Feature Selection for Detection of Ad Hoc Flooding Attacks, Advances in Intelligent and Soft Computing, Springer 2012, Volume 176, pp 507-513.
Index Terms
Keywords