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View all- AAMIR MZAIDI M(2013)A Survey on DDoS Attack and Defense Strategies: From Traditional Schemes to Current TechniquesInterdisciplinary Information Sciences10.4036/iis.2013.17319:2(173-200)Online publication date: 2013
Lightweight and distributed attack detection scheme in mobile ad hoc networks
Authors: 
R. A. Raja Mahmood, Anang Hudaya Muhamad Amin, 
Amiza Amir, Asad I. KhanAuthors Info & Claims
R. A. Raja Mahmood, Anang Hudaya Muhamad Amin, Amiza Amir, Asad I. KhanAuthors Info & ClaimsPages 162 - 169
Abstract
Many of the widely used intrusion detection schemes, such as Self Organizing Map and Artificial Immune System, require heavy computational power in order to provide highly accurate results. These schemes have been successfully deployed in wired networks, which have high computational and bandwidth capabilities. Mobile ad hoc networks, however have limited resources and hence deploying such schemes are impractical. We propose a lightweight, low-computation, distributed intrusion detection scheme for mobile ad hoc networks termed the Distributed Hierarchical Graph Neuron (DHGN). The DHGN-based network is a new form of neural network, which consists of a hierarchical graph-based representation of input patterns. This pattern recognition scheme adopts a divide-and-distribute approach that divides an input pattern into a number of subpatterns, which are then concurrently processed for recognition. The first section of this paper provides an in-depth study of mobile ad hoc networks and current intrusion detection implementation in these networks. The second section of the paper provides an overview of the proposed two-stage cooperative intrusion detection system architecture and compares the proposed algorithm with Self Organizing Map classifier. The experiments show that our low computational scheme produced similar classification accuracy results to Self Organizing Map algorithm.
References
[1]
Kohonen, T., Self-Organizing Maps. 3rd ed. Information Sciences. 2001: Springer.
[2]
S. Hofmeyr and S. Forrest. Immunity by Design: An Artificial Immune System. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Morgan-Kaufmann, San Francisco, CA, pp. 1289--1296 (1999).
[3]
Jianhong Gao, Lixin Xu, Yaping Dai. An intrusion detection system model based on self-organizing map. Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on In Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on, Vol. 5 (2004), pp. 4367--4369.
[4]
Cheng Zhang, Jing Zhang, Sunjun Liu, Yintian Liu. Network Intrusion Active Defense Model Based on Artificial Immune System," icnc, vol. 1, pp. 97--100, 2008 Fourth International Conference on Natural Computation, 2008.
[5]
Machado, R. B., Boukerche, A., Sobral, J. B. M., Juca, K. R. L., Notare, M. S. M. A. A Hybrid Artificial Immune and Mobile Agent Intrusion Detection Based Model for Computer Network Operations Parallel and Distributed Processing Symposium, 2005. Proceedings. 19th IEEE International Volume, Issue, 04--08 April 2005 Page(s): 191a - 191a.
[6]
Hongxia Xie; Zhengyun Hui. An Intrusion Detection Architecture for Ad Hoc Network Based on Artificial Immune System. Parallel and Distributed Computing, Applications and Technologies, 2006. PDCAT '06. Seventh International Conference on Volume, Issue, Dec. 2006 Page(s):1--4
[7]
Nasution, B. B. and A. I. Khan, A Hierarchical Graph Neuron Scheme for Real-Time Pattern Recognition. IEEE Transactions on Neural Networks, 2008. 19(2): p. 212--229.
[8]
Albers, P. and Camp, O. (2002). Security in Ad Hoc Networks: a General Intrusion Detection Architecture Enhancing Trust Based Approaches. The 1st International Workshop on Wireless Information Systems" (WIS-2002), ICEIS 2002
[9]
Yang, H., Luo, H. Y., Ye, F., Lu, S. W. and Zhang, L. (2004). Security in MANETs: Challenges and Solutions, IEEE Wireless Communications.
[10]
Lundin, E. and Jonsson, E. (2002). Survey of research in the intrusion detection area, Technical report 02--04, Department of Computer Engineering, Chalmers University of Technology, Göteborg.
[11]
Gerla, M., Lindemann, C. and Rowstron, A. (2005). "P2P MANETs-New Research Issues", Dagstuhl Seminar Proceedings, Dagstuhl.
[12]
Brutch, P. and Ko, C. (2003). "Challenges in Intrusion Detection for Wireless Ad-hoc Networks." Proceedings of 2003 Symposium on Applications and the Internet Workshop.
[13]
Ngai, E. C. H. and Lyu, M. R. (2004). Trust- and Clustering-Based Authentication Services in Mobile Ad Hoc Networks, icdcsw, pp. 582--587, 24th International Conference on Distributed Computing Systems Workshops - W4: MDC (ICDCSW'04)
[14]
Karygiannis, A., Antonakakis, E. and Apostolopoulos, A. (2006). Host-based Network Monitoring Tools for MANETs, ACM/IEEE MSWiM 2006, 9th Annual International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, October 2--6, 2006, Terremolinos, Spain.
[15]
Zhang, Y. and Lee, W. (2000). "Intrusion detection in wireless ad-hoc networks." Proceedings of the 6th annual international conference on Mobile computing and networking, pp. 275--283.
[16]
Kachirski, O. and Guha, R. (2002). "Effective Intrusion Detection Using Multiple Sensors in Wireless Ad Hoc Networks." Proceedings of the 36th Hawaii International Conference On System Sciences.
[17]
Sekar, R., Gupta, A., Frullo, J., Shanbhag, T., Tiwari, A., Yang, H. and Zhou, S. (2002).
[18]
Li, Y. and Wei, J. (2004). Guidelines on Selecting Intrusion Detection Methods in MANET. In The Proceedings of ISECON 2004, v 21 (Newport): §3233. ISSN: 1542--7382
[19]
Zhang, Y., Lee, W. and Huang, Y. (2003). Intrusion Detection Techniques for Mobile Wireless Networks, ACM/Kluwer Wireless Networks Journal (ACM WINET), 9, 5, pp. 545--556.
[20]
Guan, X., Yang, Y. and You, J.(2000). "POM-A Mobile Agent Security Model against Malicious Hosts", Proceedings of the 4th International Conference on High Performance Computing in the Asia-Pacific Region, pp. 1165--1166.
[21]
Huang, Y., Fan, W., Lee, W. and Yu, P. S. (2003). Cross-Feature Analysis for Detecting Ad-Hoc Routing Anomalies, Proceedings of the 23rd International Conference on Distributed Computing Systems, p. 478
[22]
Sun, B., Wu, K. and Pooch, U. (2003). "Routing anomaly detection in mobile ad hoc networks." Proceedings of the 12 th International conference on computer communications and networks, pp 20--23.
[23]
Khan A. I., "A peer-to-peer associative memory network for intelligent information systems," Proc. of the Thirteenth Australasian Conf. on Information Systems, vol. 1, 2002.
[24]
Nasution, B. B. and A. I. Khan, A Hierarchical Graph Neuron Scheme for Real-Time Pattern Recognition. IEEE Transactions on Neural Networks, 2008. 19(2): p. 212--229.
[25]
Khan, A. I. and A. H. Muhamad Amin, One Shot Associative Memory Method for Distorted Pattern Recognition, in AI 2007: Advances in Artificial Intelligence, 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2--6, 2007
[26]
Vesanto, J., Alhoniemi, E., Himberg, J., Kiviluoto, K., and Parviainen, J., "SelfOrganizing Map for Data Miningin Matlab: The SOM Toolbox," Simulation NewsEurope, vol. 25, 1999.
[27]
R. A. Raja Mahmood, A. H. Muhamad Amin and A. I. Khan, "A Distributed Hierarchical Graph Neuron-based Classifier: An Efficient, Low Computational Classifier", 1st Intl Intelligent Networks and Intelligent Systems, pp. 675--680, Nov 2008.
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December 2009
663 pages
ISBN:9781605586595
DOI:10.1145/1821748
- General Chair:
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Published: 14 December 2009
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View all- AAMIR MZAIDI M(2013)A Survey on DDoS Attack and Defense Strategies: From Traditional Schemes to Current TechniquesInterdisciplinary Information Sciences10.4036/iis.2013.17319:2(173-200)Online publication date: 2013
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