MBotCS: A mobile botnet detection system based on machine learning

Meng, X. & Spanoudakis, G. (2016). MBotCS: A mobile botnet detection system based on machine learning. Lecture Notes in Computer Science, 9572, pp. 274-291. doi: 10.1007/978-3-319-31811-0_17

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Abstract

As the use of mobile devices spreads dramatically, hackers have started making use of mobile botnets to steal user information or perform other malicious attacks. To address this problem, in this paper we propose a mobile botnet detection system, called MBotCS. MBotCS can detect mobile device traffic indicative of the presence of a mobile botnet based on prior training using machine learning techniques. Our approach has been evaluated using real mobile device traffic captured from Android mobile devices, running normal apps and mobile botnets. In the evaluation, we investigated the use of 5 machine learning classifier algorithms and a group of machine learning box algorithms with different validation schemes. We have also evaluated the effect of our approach with respect to its effect on the overall performance and battery consumption of mobile devices.

Item Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-31811-0_17
Uncontrolled Keywords: Android, Mobile Botnet, Security, Machine Learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Informatics > Department of Computing
URI: http://openaccess.city.ac.uk/id/eprint/15281

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