A New Android Malware Detection Method Using Bayesian Classification

Suleiman Y. Yerima, Sakir Sezer, Gavin McWilliams, Igor Muttik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

142 Citations (Scopus)
470 Downloads (Pure)

Abstract

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.
Original languageEnglish
Title of host publication2013 IEEE 27th International Conference on Advanced Information Networking and Applications
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages121-128
Number of pages8
DOIs
Publication statusPublished - Mar 2013
Event2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA) - Hotel Catalonia Barcelona Plaza, Barcelona, Spain
Duration: 25 Mar 201329 Mar 2013

Conference

Conference2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA)
CountrySpain
CityBarcelona
Period25/03/201329/03/2013

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  • Cite this

    Yerima, S. Y., Sezer, S., McWilliams, G., & Muttik, I. (2013). A New Android Malware Detection Method Using Bayesian Classification. In 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (pp. 121-128). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/AINA.2013.88