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Modeling and analyzing of botnet interactions

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Title: Modeling and analyzing of botnet interactions
Authors: Song, Li-Peng1 slp880@gmail.com, Jin, Zhen2 jinzhn@263.net, Sun, Gui-Quan2 gquansun@yahoo.com.cn
Source: Physica A. Jan2011, Vol. 390 Issue 2, p347-358. 12p.
Subjects: Intelligent agents, Malware, Evolutionary computation, Game theory, Computer software
Abstract: Abstract: The dynamics of interacting botnets and the effects of the strategies selected by interacting botnet owners on the spread of botnets remain unclear. As a result, in this paper, we present a botnet interaction model, obtained by coupling a fast evolutionary game dynamics to a slow population dynamics model, in which two botnet types are considered. We analyze the fast evolutionary game model and obtain two stable equilibria. Additionally, we substitute them into the complete model and get two reduced models. Such models allow us to study the effects of strategies selected by botnet owners. Analysis of the models shows that when all owners adopt the cooperative strategy both types of botnets can survive with much lower contact rates. However, while they choose the competitive strategy one type of botnet will become extinct and the other will persist with a lower infection rate. The equilibrium conditions of the evolutionary game model, which can guide us in designing effective counter-botnet methods, are also obtained. [ABSTRACT FROM AUTHOR]
Copyright of Physica A is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 55387678
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Modeling and analyzing of botnet interactions
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  Data: <searchLink fieldCode="JN" term="%22Physica+A%22">Physica A</searchLink>. Jan2011, Vol. 390 Issue 2, p347-358. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Intelligent+agents%22">Intelligent agents</searchLink><br /><searchLink fieldCode="DE" term="%22Malware%22">Malware</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+computation%22">Evolutionary computation</searchLink><br /><searchLink fieldCode="DE" term="%22Game+theory%22">Game theory</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink>
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  Data: Abstract: The dynamics of interacting botnets and the effects of the strategies selected by interacting botnet owners on the spread of botnets remain unclear. As a result, in this paper, we present a botnet interaction model, obtained by coupling a fast evolutionary game dynamics to a slow population dynamics model, in which two botnet types are considered. We analyze the fast evolutionary game model and obtain two stable equilibria. Additionally, we substitute them into the complete model and get two reduced models. Such models allow us to study the effects of strategies selected by botnet owners. Analysis of the models shows that when all owners adopt the cooperative strategy both types of botnets can survive with much lower contact rates. However, while they choose the competitive strategy one type of botnet will become extinct and the other will persist with a lower infection rate. The equilibrium conditions of the evolutionary game model, which can guide us in designing effective counter-botnet methods, are also obtained. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Physica A is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.physa.2010.10.001
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        Text: English
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      – SubjectFull: Intelligent agents
        Type: general
      – SubjectFull: Malware
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      – SubjectFull: Evolutionary computation
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      – SubjectFull: Computer software
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      – TitleFull: Modeling and analyzing of botnet interactions
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              Text: Jan2011
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