A stochastic worm model.
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| Title: | A stochastic worm model. |
|---|---|
| Authors: | Zhou, Hanxun1, Guo, Wei2 bensonbb00@163.com |
| Source: | Telecommunication Systems. Jan2017, Vol. 64 Issue 1, p135-145. 11p. |
| Subjects: | Computer worms, Computer network security, Markov processes, Internet programming, Computer viruses |
| Abstract: | Internet worm infection continues to be one of top security threats and has been widely used by botnets to recruit newbots. In order to defend against future worms, it is important to understand how worms propagate and how different scanning strategies affect worm propagation dynamics. In our study, we present a (stochastic) continuous-time Markov chain model for characterizing the propagation of Internet worms. The model is developed for uniform scanning worms, and further for local preference scanning worms and flash worms. Specifically, for uniform and local preference scanning worms, we are able to (1) provide a precise condition that determines whether the worm spread would eventually stop and (2) obtain the distribution of the total number of infected hosts. By using the same modeling approach, we reveal the underlying similarity and relationship between uniform scanning and local preference scanning worms. Finally, we validate the model by simulating the propagation of worms. [ABSTRACT FROM AUTHOR] |
| Copyright of Telecommunication Systems is the property of Springer Nature 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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| Items | – Name: Title Label: Title Group: Ti Data: A stochastic worm model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Hanxun%22">Zhou, Hanxun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Guo%2C+Wei%22">Guo, Wei</searchLink><relatesTo>2</relatesTo><i> bensonbb00@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Telecommunication+Systems%22">Telecommunication Systems</searchLink>. Jan2017, Vol. 64 Issue 1, p135-145. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+worms%22">Computer worms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+security%22">Computer network security</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+programming%22">Internet programming</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+viruses%22">Computer viruses</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Internet worm infection continues to be one of top security threats and has been widely used by botnets to recruit newbots. In order to defend against future worms, it is important to understand how worms propagate and how different scanning strategies affect worm propagation dynamics. In our study, we present a (stochastic) continuous-time Markov chain model for characterizing the propagation of Internet worms. The model is developed for uniform scanning worms, and further for local preference scanning worms and flash worms. Specifically, for uniform and local preference scanning worms, we are able to (1) provide a precise condition that determines whether the worm spread would eventually stop and (2) obtain the distribution of the total number of infected hosts. By using the same modeling approach, we reveal the underlying similarity and relationship between uniform scanning and local preference scanning worms. Finally, we validate the model by simulating the propagation of worms. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Telecommunication Systems is the property of Springer Nature 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11235-016-0164-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 135 Subjects: – SubjectFull: Computer worms Type: general – SubjectFull: Computer network security Type: general – SubjectFull: Markov processes Type: general – SubjectFull: Internet programming Type: general – SubjectFull: Computer viruses Type: general Titles: – TitleFull: A stochastic worm model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhou, Hanxun – PersonEntity: Name: NameFull: Guo, Wei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 10184864 Numbering: – Type: volume Value: 64 – Type: issue Value: 1 Titles: – TitleFull: Telecommunication Systems Type: main |
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