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Modeling propagation dynamics of social network worms

Wen, Sheng, Zhou, Wei, Zhang, Jun, Xiang, Yang, Zhou, Wanlei and Jia, Weijia 2013, Modeling propagation dynamics of social network worms, IEEE transactions on parallel and distributed systems, vol. 24, no. 8, pp. 1633-1643, doi: 10.1109/TPDS.2012.250.

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Title Modeling propagation dynamics of social network worms
Author(s) Wen, Sheng
Zhou, Wei
Zhang, JunORCID iD for Zhang, Jun
Xiang, YangORCID iD for Xiang, Yang
Zhou, WanleiORCID iD for Zhou, Wanlei
Jia, Weijia
Journal name IEEE transactions on parallel and distributed systems
Volume number 24
Issue number 8
Start page 1633
End page 1643
Total pages 11
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2013
ISSN 1045-9219
Keyword(s) modeling
propagation dynamics
social network worms
Summary 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.
Language eng
DOI 10.1109/TPDS.2012.250
Field of Research 080303 Computer System Security
080503 Networking and Communications
Socio Economic Objective 810107 National Security
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2013, IEEE
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Document type: Journal Article
Collection: School of Information Technology
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Created: Tue, 27 Aug 2013, 12:10:25 EST

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