The Community for Technology Leaders
RSS Icon
Issue No.10 - Oct. (2013 vol.24)
pp: 1994-2003
Rossano Gaeta , Università di Torino, Torino
Marco Grangetto , Università di Torino, Torino
Peer-to-peer streaming has witnessed a great success thanks to the possibility of aggregating resources from all participants. Nevertheless, performance of the entire system may be highly degraded due to the presence of malicious peers that share bogus data on purpose. In this paper, we propose to use a statistical inference technique, namely, belief propagation (BP), to estimate the probability of peers being malicious. The detection algorithm is run by a set of trusted monitor nodes that receives notification messages (checks) from peers whenever they obtain a chunk of data; these checks contain the list of the chunk uploaders and a flag to mark the chunk as polluted or clean. Peers are able to detect if the received chunk is polluted or not but, since multiparty download is employed, they are not capable to identify the source(s) of bogus blocks. This problem definition allows us to define a factor graph of peers and checks on which an incremental version of the belief propagation algorithm is run by the monitor nodes to infer the probability of each peer being a malicious one. We evaluate the accuracy, robustness, and complexity of our technique by running a real peer-to-peer application on PlanetLab. We show that the proposed approach is very accurate and robust against malicious nodes misbehaving (different pollution intensity, presence of fake checks, churning, and total uncooperation from malicious nodes), increasing number and colluding behavior of malicious nodes.
Peer to peer computing, Monitoring, Equations, Computer architecture, Pollution, Belief propagation, Encoding, PlanetLab, Peer-to-peer, pollution attack, malicious node identification, streaming, belief propagation, statistical inference
Rossano Gaeta, Marco Grangetto, "Identification of Malicious Nodes in Peer-to-Peer Streaming: A Belief Propagation-Based Technique", IEEE Transactions on Parallel & Distributed Systems, vol.24, no. 10, pp. 1994-2003, Oct. 2013, doi:10.1109/TPDS.2012.342
[1] X. Zhang, J. Liu, B. Li, and T. Yum, "CoolStreaming/DONet: A Data-Driven Overlay Network for Efficient Live Media Streaming," Proc. IEEE INFOCOM, vol. 3, pp. 13-17, 2005.
[2] G. Huang, "PPLive: A Practical P2P Live System with Huge Amount of Users," Proc. ACM SIGCOMM Workshop Peer-to-Peer Streaming and IPTV Workshop, 2007.
[3] P. Dhungel, X. Hei, K. Ross, and N. Saxena, "The Pollution Attack in P2P Live Video Streaming: Measurement Results and Defenses," Proc. Workshop Peer-to-Peer Streaming and IP-TV (P2P-TV '07), pp. 323-328, 2007.
[4] J. Liang, R. Kumar, Y. Xi, and K. Ross, "Pollution in P2P File Sharing Systems," Proc. IEEE INFOCOM, vol. 2, pp. 1174-1185, Mar. 2005.
[5] M.N. Krohn, M.J. Freedman, and D. Mazieres, "On-the-Fly Verification of Rateless Erasure Codes for Efficient Content Distribution," Proc. IEEE Symp. Security and Privacy, 2004.
[6] C. Gkantsidis and P. Rodriguez, "Cooperative Security for Network Coding File Distribution," Proc. IEEE INFOCOM, 2006.
[7] Q. Li, D.-M. Chiu, and J. Lui, "On the Practical and Security Issues of Batch Content Distribution via Network Coding," Proc. IEEE 14th Int'l Conf. Network Protocols (ICNP '06), 2006.
[8] D. Kamal, D. Charles, K. Jain, and K. Lauter, "Signatures for Network Coding," Proc. 40th Ann. Conf. Information Sciences and Systems, 2006.
[9] Z. Yu, Y. Wei, B. Ramkumar, and Y. Guan, "An Efficient Signature-Based Scheme for Securing Network Coding against Pollution Attacks," Proc. IEEE INFOCOM, 2008.
[10] E. Kehdi and B. Li, "Null Keys: Limiting Malicious Attacks via Null Space Properties of Network Coding," Proc. IEEE INFOCOM, 2009.
[11] Z. Yu, Y. Wei, B. Ramkumar, and Y. Guan, "An Efficient Scheme for Securing XOR Network Coding against Pollution Attacks," Proc. IEEE INFOCOM, 2009.
[12] T. Ho, B. Leong, R. Koetter, M. Medard, M. Effros, and D. Karger, "Byzantine Modification Detection in Multicast Networks with Random Network Coding," IEEE Trans. Information Theory, vol. 54, no. 6, pp. 2798-2803, June 2008.
[13] S. Jaggi, M. Langberg, S. Katti, T. Ho, D. Katabi, M. Medard, and M. Effros, "Resilient Network Coding in the Presence of Byzantine Adversaries," IEEE Trans. Information Theory, vol. 54, no. 6, pp. 2596-2603, June 2008.
[14] R. Koetter and F. Kschischang, "Coding for Errors and Erasures in Random Network Coding," IEEE Trans. Information Theory, vol. 54, no. 8, pp. 3579-3591, Aug. 2008.
[15] B. Beverly Yang and H. Garcia-Molina, "Designing a Super-Peer Network," Proc. 19th Int'l Conf. Data Eng. pp. 49-60, 2003.
[16] Y. Chawathe, S. Ratnasamy, L. Breslau, N. Lanham, and S. Shenker, "Making Gnutella-Like P2P Systems Scalable," Proc. Conf. Applications, Technologies, Architectures, and Protocols for Computer Comm., pp. 407-418, 2003.
[17] D. MacKay, Information Theory, Inference and Learning Algorithms. Cambridge Univ. Press, 2003.
[18] J. Yedidia, W. Freeman, and Y. Weiss, "Constructing Free-Energy Approximations and Generalized Belief Propagation Algorithms," IEEE Trans. Information Theory, vol. 51, no. 7, pp. 2282-2312, July 2005.
[19] Q. Wang, L. Vu, K. Nahrstedt, and H. Khurana, "MIS: Malicious Nodes Identification Scheme in Network-Coding-Based Peer-t-Peer Streaming," Proc. IEEE INFOCOM, pp. 1-5, Mar. 2010.
[20] Y. Li and J.C. Lui, "Stochastic Analysis of a Randomized Detection Algorithm for Pollution Attack in P2P Live Streaming Systems," Performance Evaluation, vol. 67, no. 11, pp. 1273-1288, 2010.
[21] X. Jin and S.-H.G. Chan, "Detecting Malicious Nodes in Peer-to-Peer Streaming by Peer-Based Monitoring," ACM Trans. Multimedia Computing, Comm., and Applications, vol. 6, pp. 9:1-9:18, Mar. 2010.
[22] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., 1988.
[23] J. Yedidia, W. Freeman, and Y. Weiss, "Understanding Belief Propagation and its Generalizations," Exploring Artificial Intelligence in the New Millennium, ch. 8, Elsevier, 2003.
[24] R. Gallager, Low-Density Parity-Check Codes. M.I.T. Press, 1963.
[25] W.T. Freeman, E.C. Pasztor, and O.T. Carmichael, "Learning Low-Level Vision," Int'l J. Computer Vision, vol. 40, pp. 25-47, 2000.
[26] A. Magnetto, R. Gaeta, M. Grangetto, and M. Sereno, "P2P Streaming with LT Codes: A Prototype Experimentation," Proc. ACM Workshop Advanced Video Streaming Techniques for Peer-to-Peer Networks and Social Networking, pp. 7-12, 2010.
[27] M. Luby, "LT Codes," Proc. IEEE 43rd Symp. Foundations of Computer Science (FOCS), pp. 271-280, Nov. 2002.
[28] V. Bioglio, R. Gaeta, M. Grangetto, and M. Sereno, "On the Fly Gaussian Elimination for LT Codes," IEEE Comm. Letters, vol. 13, no. 2, pp. 953-955, Dec. 2009.
[29] Y. Weiss and W. Freeman, "On the Optimality of Solutions of the Max-Product Belief-Propagation Algorithm in Arbitrary Graphs," IEEE Trans. Information Theory, vol. 47, no. 2, pp. 736-744, Feb. 2001.
20 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool