A New Android Malware Detection Approach Using Bayesian Classification
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.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Yerima, S. Y., Sezer, S., McWilliams, G., Muttik, I. (2013) A new android malware detection approach using bayesian classification. In: Proceedings of the IEEE 27th International Conference on Advanced Information Networking and Applications, Barcelona, Spain, 25-28 March, 2013. pp. 121-128.
Research Group : Cyber Technology Institute (CTI)
Research Institute : Cyber Technology Institute (CTI)
Peer Reviewed : Yes