In modern days, more and more community joins social networks to contribute to information with others. At the same time, the in sequence sharing/spreading becomes far more frequent and convenient due to the wide usage. The research contented of computer networks comprises arrangement topology, network interchange uniqueness, and the authority of the network behavior on the whole set of connections. The spread and avoidance of network malware knowledge studied in network and have been one of the majority prolific fields in complex network dynamics research. Through our research, we found that some individuality of workstation network virus proliferation is similar to real world outbreak spread. Therefore, any misinformation should be exposed in time when it does not increase to a large group of populace. All preceding works deliberate either how the in succession is extend in the social complex or how to inhibit the further pervasion of an observed misinformation. However, no works considered how to discover the broadcasting of misinformation in time. A possible explanation is to set observers across the network to determine the suspects of misinformation established by the optimization problematic is NP-hard and deliver approximation assurances for an avaricious answer for various meanings of this problem by provides evidence that they are sub modular. In this accomplishment, a novel method to decide on a set of spectator in a social network with the minimum cost, where these observers assurance any misinformation can be discovered with a high likelihood before it reaches a surrounded number of users.

Information sharing, Misinformation, Online Social Network, Suspects, Optimization, Privacy

[1] M. Fossi and J. Blackbird, “Symantec internet security threat report 2013,” Symantec Corporation, Tech. Rep., April, 2014.
[2] C. C. Zou, D. Towsley, and W. Gong, “Modeling and simulation study of the propagation and defense of internet e-mail worms,” Dependable and Secure Computing, IEEE Transactions on, vol. 4, no. 2, pp. 105–118, 2007.
[3] Z. Chen and C. Ji, “Spatial-temporal modeling of malware propagation in networks,” Neural Networks, IEEE Transactions on, vol. 16, no. 5, pp. 1291–1303, 2005.
[4] S. Wen, W. Zhou, J. Zhang, Y. Xiang, W. Zhou, and W. Jia, “Modeling propagation dynamics of social network worms,” Parallel and Distributed Systems, IEEE Transactions on, vol. 24, no. 8, pp. 1633–1643, 2013.
[5] Y. Cao, V. Yegneswaran, P. A. Porras, and Y. Chen, “Pathcutter: Severing the self-propagation path of xss javascript worms in social web networks.” in NDSS, 2012.
[6] M. R. Faghani and U. T. Nguyen, “A study of xss worm propagation and detection mechanisms in online social networks,” Information Forensics and Security, IEEE Transactions on, vol. 8, no. 11, pp. 1815–1826, 2013.
[7] C. Song, T. Koren, P. Wang, and A.-L. Barab´asi, “Modelling the scaling properties of human mobility,” Nature Physics, vol. 6, no. 10, pp. 818–823, 2010.
[8] A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, “Measurement and analysis of online social networks,” in Proceedings of the 7th ACM SIGCOMM conference on Internet measurement. ACM, 2007, pp. 29–42.
[9] M. E. Newman, S. Forrest, and J. Balthrop, “Email networks and the spread of computer viruses,” Physical Review E, vol. 66, no. 3, p. 035101, 2002.
[10] Y.-Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong, “Analysis of topological characteristics of huge online social networking services,” in Proceedings of the 16th international conference on World Wide Web. ACM, 2007, pp. 835–844.
[11] R. Pastor-Satorras and A. Vespignani, “Epidemic dynamics in finite size scale-free networks,” Physical Review E, vol. 65, no. 3, p. 035108, 2002.
[12] M. Bogun´a, R. Pastor-Satorras, and A. Vespignani, “Epidemic spreading in complex networks with degree correlations,” in Proceedings of the XVIII Sitges Conference on Statistical Mechanics, Lecture Notes in Physics, Springer, Berlin, 2003.
[13] J. O. Kephart and S. R. White, “Directed-graph epidemiological models of computer viruses,” in Research in Security and Privacy, 1991. Proceedings., 1991 IEEE Computer Society Symposium on. IEEE, 1991, pp. 343–359.
[14] D. Chakrabarti, J. Leskovec, C. Faloutsos, S. Madden, C. Guestrin, and M. Faloutsos, “Information survival threshold in sensor and p2p networks,” in INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE. IEEE, 2007, pp. 1316–1324.
[15] G. Yan, G. Chen, S. Eidenbenz, and N. Li, “Malware propagation in online social networks: nature, dynamics, and defense implications,” in Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security. ACM, 2011, pp. 196–206.