PTP Approach in Network Security for Misbehavior Detection
Abstract
A PTP approach in network security for misbehavior detection system present a method for detecting malicious misbehavior activity within networks. Along with the detection, it also blocks the malicious system within the network and adds it to Blacklist. Malicious node defined as a compromised machine within the network that performs the task provided by bot server i.e. it does not forward the legitimate message to another node in the network or send some other message to a neighbor node. This system is based on Probabilistic threat propagation. This scheme is used in graph analysis for community detection. The proposed system enhances the prior community detection work by propagating threat probabilities across graph nodes. To demonstrate Probabilistic Threat Propagation (PTP) paper considers the task of detecting malicious node in the network. Proposed System also shows the relationship between PTP and loopy belief propagation.
Downloads
References
[2] B. Coskun, S. Dietrich, and N. Memon, “Friends of an enemy: Identifying local members of peer-to-peer botnets using mutual contacts,” in Proc. 26th Annu. Comput. Security Appl. Conf., Dec. 2010.
[3] G. Gu, J. Zhang, and W. Lee, “BotSniffer: Detecting botnet command and control channels in network traffic,” in Proc. 15th Annu. Network.Distributed.System.Security. (NDSS), Feb. 2008.
[4] J. D. Lafferty, A. McCallum, and F. C. N. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in
Proc. 8th Int. Conf. Mach. Learn. (ICML), 2001.
[5] S. Philips, E. Kao, M. Yee, and C. Anderson, “Detecting activity-based communities using dynamic membership propagation,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process., Mar. 2012.
[6] J. Zhang, P. Porras, and J. Ullrich, “Highly predictive blacklisting,” in
Proc. 17th Conf. Security Symp., 2008
[7] M. P. Collins and M. K. Reiter, “On the limits of payload-oblivious network attack detection,” in Proc. 11th Int. Symp.Recent Adv. IntrusionDetection (RAID), 2008.
[8] M. Roesch, “SNORT—Lightweight intrusion detection for networks,” in
Proc. 13th LISA Conf., 1999.
[9] Haojin Zhu, Suguo Du, ZhaoyuGao, Mianxiong Dong and Zhenfu Cao, “A
Probabilistic Misbehavior Detection Scheme toward Efficient Trust Establishment in Delay-Tolerant Networks”, IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 1, JANUARY 2014.
[10] K. M. Carter, N. Idika, and W. W. Streilein, “Probabilistic threat propagation for malicious activity detection,” in Proc. IEEE Int. Conf.Acoust., Speech Signal Process., May 2013.
[11] RuifangLiua, Shan Fenga, RuishengShib,, WenbinGuoa, “Weighted graph clustering for community detection of large social networks,” in 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014.
Copyright (c) 2020 IJRDO - Journal of Computer Science Engineering (ISSN: 2456-1843)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties, and that the Article has not been published elsewhere. Author(s) agree to the terms that the IJRDO Journal will have the full right to remove the published article on any misconduct found in the published article.
