Abstract
The complex interactions among internet worms have great impact on the dynamics of worms. To contain the propagation of worms, it is necessary to characterize these interactions. Therefore, a two‐worm interaction model is presented in this paper. Different from previous researches, we have considered the influence of adaptive human reaction stirred by one cooperative worm on the other worm in the model. The model’s equilibria and their stability conditions are obtained mathematically and verified by simulations. Results indicate that considering adaptive human behavior significantly changes the prospective propagation course of worms and that this consideration has implications for designing counterworm methods.
Highlights
Nowadays, malware including worms, viruses, botnets et al is prevalent on the internet, which has led to serious problems to the security of internet
Han and Tan 3 analyzed the influence of time delay on computer virus by using a susceptible-infected-recovered-susceptible model
3.3a and 3.3b give the noncooperative worm’s existence thresholds when the cooperative worm dies out or exists, respectively, that is, to ensure the existence of noncooperative worm, β2 must be greater than the threshold value δ predicted by R20 1 cooperative worm dies out or the threshold value β1 − δ1 δ δ1N/ δ1N μI1∗ β1 − δ1 δ predicted by R102 1 cooperative worm exists
Summary
Malware including worms, viruses, botnets et al is prevalent on the internet, which has led to serious problems to the security of internet. Han and Tan 3 analyzed the influence of time delay on computer virus by using a susceptible-infected-recovered-susceptible model. Song et al 4 presented a model focusing on the worms spreading via both Web-based scanning and removable devices. They found that the existence of infected removable devices was in favor of the outbreak of worms, and limiting the number of removable devices would prevent the worms’ outbreak. Some other models 7–10 have been given in recent year All of these studies have focused on one type of malware. We present a two-worm model to analyze the influence of one cooperative worm on the other worm.
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