An Approach for Defending Against Collaborative Attacks by Malicious Nodes in MANETs: A Cooperative Bait Detection Approach
Keywords:
Mobile Nodes, Routing, Node discoveryAbstract
In mobile ad-hoc networks (MANETs), an essential requirement for the foundation of communication among nodes is that nodes should coordinate with one another. In the presence of malicious nodes, this requirement may lead serious security concerns; for instance, such node may disturb the routing process. In this context, preventing or detecting malicious nodes launching grayhole or collaborative black hole in challenge. This project attempts to determine this issue by designing a dynamic source routing (DSR)- based routing mechanism, which is referred to as the cooperative bait detection scheme(CBDS), that coordinates the advantages of both proactive and reactive defence architectures. Our CBDS system implements a reverse tracing technique to help in achieving the stated goal. Simulation results are provided, showing that in the presence of malicious node attacks, the CBDS outperforms the DSR, 2ACK, and best-effort fault-tolerant routing (BFTR) protocols (chosen as benchmarks) in terms of packet delivery ratio and routing overhead (chosen as performance metrics).
References
[1] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res., pp. 993–1022, 2003.
[2] Y. Ge, H. Xiong, C. Liu, and Z.-H. Zhou, “A taxi driving fraud detection system,” in Proc. IEEE 11th Int. Conf. Data Mining, 2011, pp. 181–190.
[3] D. F. Gleich and L.-h. Lim, “Rank aggregation via nuclear norm minimization,” in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2011, pp. 60–68.
[4] A. Klementiev, D. Roth, K. Small, and I. Titov, “Unsupervised rank aggregation with domain-specific expertise,” in Proc. 21 st Int. Joint Conf. Artif. Intell., 2009, pp. 1101–1106.
[5] A. Klementiev, D. Roth, and K. Small, “Unsupervised rank aggregation with distance-based models,” in Proc. 25th Int. Conf. Mach. Learn., 2008, pp. 472–479
[6] Hengshu Zhu, Hui Xiong, Senior Member, IEEE, Yong Ge, and Enhong Chen, Senior Member, IEEE, “Discovery of Ranking Fraud for Mobile Apps”, vol.13,n0.1,Jan 2015 XIII.
[7] A. Ntoulas, M. Najork, M. Manasse, and D. Fetterly, “Detecting spam web pages through content analysis,” in Proc. 15th Int. Conf. World Wide Web, 2006, pp. 83–92.
[8] N. Spirin and J. Han, “Survey on web spam detection: Principles and algorithms,” SIGKDD Explor. Newslett., vol. 13, no. 2, pp. 50– 64, May 2012.
[9] B. Zhou, J. Pei, and Z. Tang, “A spamicity approach to web spam detection,” in Proc. SIAM Int. Conf. Data Mining, 2008, pp. 277–288.
[10] E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw, “Detecting product review spammers using rating behaviors,” in Proc. 19thACMInt. Conf. Inform. Knowl. Manage., 2010, pp. 939–948..
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