Journal
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
Volume 24, Issue 8, Pages 1633-1643Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2012.250
Keywords
Security; social network worms; propagation dynamics; modeling
Funding
- General Research Fund of the Hong Kong SAR, China [CityU 114609, CityU 114012]
- CityU Applied RD Funding (ARD) [9681001]
- CityU Strategic Research Grant [7008110]
- CityU Applied Research Grant (ARG) [9667052]
- ShenZhen-HK Innovation Cycle Grant [ZYB200907080078A]
- NSF (China) [61070222]
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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.
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