PIndroid: A novel Android malware detection system using ensemble learning methods
Idrees, F., Rajarajan, M., Conti, M. , Chen, T. & Rahulamathavan, Y. (2017). PIndroid: A novel Android malware detection system using ensemble learning methods. Computers and Security, 68, pp. 36-46. doi: 10.1016/j.cose.2017.03.011
Abstract
The extensive use of smartphones has been a major driving force behind a drastic increase of malware attacks. Covert techniques used by the malware make them hard to detect with signature based methods. In this paper, we present PIndroid – a novel Permissions and Intents based framework for identifying Android malware apps. To the best of our knowledge, PIndroid is the first solution that uses a combination of permissions and intents supplemented with Ensemble methods for accurate malware detection. The proposed approach, when applied to 1,745 real world applications, provides 99.8% accuracy (which is best reported to date). Empirical results suggest that the proposed framework is effective in detection of malware apps.
| Publication Type: | Article |
|---|---|
| Publisher Keywords: | Malware classification; Permissions; Intents; Ensemble methods; Colluding applications |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Departments: | School of Science & Technology > Engineering |
| SWORD Depositor: |
Available under License Creative Commons Attribution Non-commercial No Derivatives.
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