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ABSTRACT
The alarm that worms start to spread on increasingly popular mobile devices calls for an in-depth investigation of their propagation dynamics. In this paper, we study how mobility patterns affect Bluetooth worm spreading speeds. We find that the impact of mobility patterns is substantial over a large set of of changing Bluetooth and worm parameters. For instance, a mobility model under which devices move among a fixed set of activity locations can result in worm propagation speeds four times faster than a classical mobility model such as the random walk model. Our investigation reveals that the key factors affecting Bluetooth worm propagation speeds include spatial distributions of nodes, link duration distributions, degrees to which devices are mixed together, and even the burstiness of successive links.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 2
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Abhijit Bose , Xin Hu , Kang G. Shin , Taejoon Park, Behavioral detection of malware on mobile handsets, Proceeding of the 6th international conference on Mobile systems, applications, and services, June 17-20, 2008, Breckenridge, CO, USA
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Karthik Channakeshava , Deepti Chafekar , Keith Bisset , V. S. Anil Kumar , Madhav Marathe, EpiNet: a simulation framework to study the spread of malware in wireless networks, Proceedings of the 2nd International Conference on Simulation Tools and Techniques, March 02-06, 2009, Rome, Italy
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