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Network immunization and virus propagation in email networks: experimental evaluation and analysis

Network immunization and virus propagation in email networks: experimental evaluation and analysis,10.1007/s10115-010-0321-0,Knowledge and Information

Network immunization and virus propagation in email networks: experimental evaluation and analysis   (Citations: 2)
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Network immunization strategies have emerged as possible solutions to the challenges of virus propagation. In this paper, an existing interactive model is introduced and then improved in order to better characterize the way a virus spreads in email networks with different topologies. The model is used to demonstrate the effects of a number of key factors, notably nodes’ degree and betweenness. Experiments are then performed to examine how the structure of a network and human dynamics affects virus propagation. The experimental results have revealed that a virus spreads in two distinct phases and shown that the most efficient immunization strategy is the node-betweenness strategy. Moreover, those results have also explained why old virus can survive in networks nowadays from the aspects of human dynamics.
Journal: Knowledge and Information Systems - KAIS , vol. 27, no. 2, pp. 253-279, 2011
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    • ...In order to help researchers observe and predict the poten­ tial damages of a virus, some models have been used to study the dynamic process of virus propagation [5][6][7][8][9] [10][11]...
    • ...Valid propagation models can provide effective test-beds for developing or evaluating new and/or improved security strategies for restraining virus propagation [5][6]...
    • ...The propagation of SMS-based viruses in mobile networks follows a long-range spreading pattern that is similar to the spreading of computer viruses, such as worm propagation in email networks [6][22]...
    • ...Briefly, once the sample size goes to infinity, the message-clicking probabilities among different users follow a Gaussian distribution [6][22][29], i.e., Vi.Pclick "'(fl, (j2), where fl = 0.5 and (j = 0.3 in our model...
    • ...We have provided more comparing results about the effects of network struc­ tures on the e-mail worm propagation in [6]...

    Chao Gaoet al. Modeling and predicting the dynamics of mobile virus spread affected b...

    • ...Currently, there have been many studies on modeling virus propagation, including agent-based models [1][2] and population-based models [3][4], and on designing effective immunization strategies for restraining virus propagation [5][6][7][8][9][10]...
    • ...These propagation models have provided feasible test-beds for examining the mechanisms of virus propagation and for evaluating new and/or improved security strategies for restraining virus propagation [2]...
    • ...Fig. 3 in [2] provides an illustration of the process of a static strategy...
    • ...Then, we use an improved interactive email propagation model [2], as a test-bed to evaluate whether the adaptive AOC-based strategy can protect dynamically-evolving email networks...
    • ...By analyzing the email-checking intervals in the Enron email dataset [2] and related studies on human dynamics [12][13][14], we have found that the emailbox checking intervals of a user follow a power-law distribution with a long tail...
    • ...Different from the traditional interactive email model [1], our interactive email model incorporates two further changes: (1) The email-checking intervals of a user follow a power-law distribution based on our previous research on human dynamics [2]; (2) We extend the states of each node in the interactive email model [1][2]...
    • ...Different from the traditional interactive email model [1], our interactive email model incorporates two further changes: (1) The email-checking intervals of a user follow a power-law distribution based on our previous research on human dynamics [2]; (2) We extend the states of each node in the interactive email model [1][2]...
    • ...In this paper, we utilize an improved interactive email model [2] as a test-bed to evaluate the efficiency of the adaptive AOC-based immunization strategy in both static benchmark and dynamically-evolving synthetic networks...
    • ...The distribution exponent (i.e., α ≈ 1.3 ±0.5) is based on our previous research [2]...
    • ...That is because viruses will stay at a latent state and await activation by users [2]...
    • ...We have evaluated our proposed strategy on the improved interactive email model [2] in this paper, and on a mobile model [19] (The results are not reported here for the page limitation)...

    Jiming Liuet al. Adaptive Immunization in Dynamic Networks

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