Issue No. 08 - Aug. (2013 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.250
Sheng Wen , Central South University, Changsha and Deakin University, Melbourne
Wei Zhou , Central South University, Changsha and Deakin University, Melbourne
Jun Zhang , Deakin University, Melbourne
Yang Xiang , Deakin University, Melbourne
Wanlei Zhou , Deakin University, Melbourne
Weijia Jia , City University of Hong Kong, Hong Kong
Social network worms, such as email worms and facebook worms, pose a critical security threat to the Internet. Modeling their propagation dynamics is essential to predict their potential damages and develop countermeasures. Although several analytical models have been proposed for modeling propagation dynamics of social network worms, there are two critical problems unsolved: temporal dynamics and spatial dependence. First, previous models have not taken into account the different time periods of Internet users checking emails or social messages, namely, temporal dynamics. Second, the problem of spatial dependence results from the improper assumption that the states of neighboring nodes are independent. These two problems seriously affect the accuracy of the previous analytical models. To address these two problems, we propose a novel analytical model. This model implements a spatial-temporal synchronization process, which is able to capture the temporal dynamics. Additionally, we find the essence of spatial dependence is the spreading cycles. By eliminating the effect of these cycles, our model overcomes the computational challenge of spatial dependence and provides a stronger approximation to the propagation dynamics. To evaluate our susceptible-infectious-immunized (SII) model, we conduct both theoretical analysis and extensive simulations. Compared with previous epidemic models and the spatial-temporal model, the experimental results show our SII model achieves a greater accuracy. We also compare our model with the susceptible-infectious-susceptible and susceptible-infectious-recovered models. The results show that our model is more suitable for modeling the propagation of social network worms.
Social network services, Grippers, Computational modeling, Topology, Analytical models, Network topology, Electronic mail, propagation dynamics, Social network services, Grippers, Computational modeling, Topology, Analytical models, Network topology, Electronic mail, modeling, Security, social network worms
S. Wen, W. Jia, W. Zhou, Y. Xiang, W. Zhou and J. Zhang, "Modeling Propagation Dynamics of Social Network Worms," in IEEE Transactions on Parallel & Distributed Systems, vol. 24, no. , pp. 1633-1643, 2013.