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Epidemiological modeling of network worm infections and countermeasures

Epidemiological modeling of network worm infections and countermeasures

Shahzad, Khurram and Woodhead, Steve (2016) Epidemiological modeling of network worm infections and countermeasures. In: 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, pp. 628-633. ISBN 978-1-5090-1949-6 ISSN 978-1-5090-1950-2 (Online) (doi:10.1109/IntelCIS.2015.7397288)

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Abstract

Fast spreading network worms continue to pose a threat to the Internet due to their virulence, speed and the con-tinuous discovery of wormable vulnerabilities. Mathematical models for worm propagation can help us to understand the epidemiology of worm outbreaks and to devise effective defense mechanisms. In this paper, we report the epidemiological modeling and analysis of worm propagation and a distributed worm detection and prevention countermeasure. The work is based on the Slammer and Witty worm characteristics and employs the widely used Susceptible-Infected biological model. The epidemiological modeling shows that the SI model can be used to represent the virulence of random scanning worms and to quantify the effectiveness of worm countermeasures.

Item Type: Conference Proceedings
Title of Proceedings: 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)
Additional Information: Conference held from 12 - 14 December 2015, Cairo, Egypt.
Uncontrolled Keywords: Worms; Slammer; Witty, Susceptible-Infected (SI); Modeling; Grippers; Logic gates; Limiting; Analytical models; Yttrium; Frequency locked loops; Network Security; Malware
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Faculty of Engineering & Science > Internet Security Research Laboratory
Related URLs:
Last Modified: 19 Oct 2016 08:31
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/15870

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