Low Power Distributed MAC Protocol Against Various Kinds Of Attacks By Using Traffic Analysis Methodology

T. Dheepak¹ , S. Neduncheliyan²

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.3 , pp.1-7, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.17

Online published on Jun 30, 2018

Copyright © T. Dheepak, S. Neduncheliyan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The mobile Ad-hoc network (MANET) lacks fixed infrastructure every node communicates with other nodes. The nodes are therefore prevented from crime attack and at the same time the approach called marking on demand is used for the sake of security purpose. The approach called marking on demand is helpful into the MANET for the functions like message sending and data transmission. The MOD (Marking on Demand) Approach is more effective and entirely different from OPM(Opportunistic Piggyback Marking) and MBT (Marking Based Trace back) which have been already existing. The performance of trace back message and message delivery has not been effective. The aim of this paper is to propose the mechanism known as deterministic packet marking for the sake of tracking out the attack which involves in the MANET. The attack , therefore has to be prevented by using MOD and DBT mechanism. The main purpose of the DPM is to investigate and find the solution for the problem of mitigate and counter message. The technical execution is demonstrated through our design with the help of mathematical model, by which the packet delay is reduced. By this approach the packet delivery ratio and the throughput with the energy level are higher in the states of consumption and efficiency.

Key-Words / Index Term :
Crime attack, Mitigate Counter Message, Traffic analysis, MANET

References :
[1] Fan Zhang, Wenbo He, Yangyi Chen, Zhou Li, XiaoFeng Wang, Shuo Chen, and Xue Liu,” Thwarting Wi-Fi Side-Channel Analysis through Traffic Demultiplexing”, IEEE Ttransactions on Wireless Communications, vol. 13, no. 1, january 2014.
[2] Mohanapriya Marimuthu and Ilango Krishnamurthi,” Enhanced OLSR for Defense against DOS Attack in Ad Hoc Networks”, journal of Communications and Networks, vol. 15, no. 1, february 2013.
[3] Jerzy Konorski and Szymon Szott,” Discouraging Traffic Remapping Attacksin Local Ad Hoc Networks”, IEEE Transactions on Wireless Communications, vol. 13, no. 7, July 2014.
[4] Leovigildo Sánchez-Casado, Gabriel Maciá-Fernández and Pedro García-Teodoro,” An Efficient Cross-Layer Approach for Malicious Packet Dropping Detection in MANETs”, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications.
[5] Hamid Alipour, Youssif B. Al-Nashif, Pratik Satam, and Salim Hariri, “Wireless Anomaly Detection Based on
IEEE 802.11 Behavior Analysis”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 10, NO. 10, OCTOBER 2015.
[6] Long Cheng, Dinil Mon Divakaran, Wee Yong Lim, Vrizlynn L. L. Thing,” Opportunistic Piggyback Marking for IP Traceback”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 2, FEBRUARY 2016.
[7] Prasenjit Choudhury,Subrata Nandi,Anita Pal, Narayan C. Debnath,” Mitigating Route Request Flooding Attack in
MANET using Node Reputation, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 2, FEBRUARY 2012.
[8] T Divya Sai Keerthi, Pallapa Venkataram,” Locating the Attacker of Wormhole Attack by Using the Honeypot”, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications.
[9] Hamid Alipour, Youssif B. Al-Nashif, Pratik Satam, and Salim Hariri,” Wireless Anomaly Detection Based on IEEE 802.11 Behavior Analysis”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 10, NO. 10, OCTOBER 2015.
[10] B. Kannhavong, H. Nakayama, Y. Nemoto, N. Kato, A. Jamalipour. A survey of routing attacks in mobile ad hoc networks. Security in wireless mobile ad hoc and sensor networks, October 2007, page, 85-91
[11] Z. Karakehayov, “Using REWARD to Detect Team BlackHole Attacks in Wireless Sensor Networks,” Wksp. Real-World Wireless Sensor Networks, June 20–21, 2005.
[12] S. Desilva, and R. V. Boppana, “Mitigating Malicious Control Packet Floods in Ad Hoc Networks,” Proc. IEEE Wireless Commun. and Networking Conf., New Orleans, LA, 2005.
[13] S. Kurosawa et al., “Detecting Blackhole Attack on AODVBased Mobile Ad Hoc Networks by Dynamic Learning Method,” Proc. Int’l. J. Network Sec., 2006.
[14] D. Johnson and D. Maltz, “Dynamic Source Routing in Ad Hoc Wireless Networks,” Mobile Computing, T. Imielinski and H. Korth, Ed., pp. 153-81. Kluwer, 1996.
[15] Jyoti Raju and J.J. Garcia-Luna-Aceves, “ A comparison of On-Demand and Table-Driven Routing for Ad Hoc Wireless etworks’,” in Proceeding of IEEE ICC, June 2000.
[16] Y-C. Hu, A. Perrig, and D. Johnson, “Wormhole Attacks in Wireless Networks,” IEEE JSAC, vol. 24, no. 2, Feb. 2006.
[17] M. Al-Shurman, S-M. Yoo, and S. Park, “Black Hole Attack in Mobile Ad Hoc Networks,” ACM Southeast Regional Conf. 2004.
[18] M. G. Zapata and N. Asokan, “Securing Ad-Hoc Routing Protocols,” Proc. 2002 ACM Wksp. W[12] M. G. Zapata and N. Asokan, “Securing Ad-Hoc Routing Protocols,” Proc. 2002 ACM Wksp. Wireless Sec., Sept. 2002, pp. 1–10.
[19] K. Sanzgiri et al., “A Secure Routing Protocol for Ad Hoc Networks,” Proc. 2002 IEEE Int’l. Conf. Network Protocols, Nov. 2002.
[20] C. Perkins and E Royer, “Ad Hoc On-Demand Distance Vector Routing,” 2nd IEEE Wksp. Mobile Comp. Sys. and Apps., 1999.
[21] Y. Xie and S.Z. Yu, “Monitoring the Application-Layer DDoS Attacks for Popular Websites,” IEEE/ACM Transactions on Networking, vol. 17, no. 1, pp. 15-25, February 2009.
[22] Y. Chen and K. Hwang, “Spectral Analysis of TCP Flows for Defense against Reduction-of-Quality Attacks,” in Proceedings of the 2007 IEEE International Conference on Communications (ICC’07), pp. 1203–1210, June 2007.
[23] F. Yi, S. Yu, W. Zhou, J. Hai and A. Bonti, “Source-Based Filtering Algorithms against DDoS Attacks,” International Journal of Database Theory and Applications, vol. 1, no. 1, pp. 9-22, 2008.
[24] S. Yu, T. Thapngam, J. Liu, S. Wei and W. Zhou, “Discriminating DDoS Flows from Flash Crowds Using Information Distance,” in Proceedings of the 3rd IEEE International Conference on Network and System Security (NSS’09), 18-21 October 2009.
[25] Y. Chen and K. Hwang, "Collaborative detection and filtering of shrew DDoS attacks using spectral analysis,” Journal of Parallel and Distributed Computing, vol. 66, no. 9, pp. 1137-1151, September 2006.
[26] T. Tuncer and Y. Tatar, “Detection SYN Flooding Attacks Using Fuzzy Logic,” in Proceedings of International Conference on Information Security and Assurance (ISA’08), pp. 321-325, 24-26 April 2008.