Machine Learning Aided Malware Detection With Focus On Android

Activity: Talk or presentationOral presentation


The widespread adoption of Android devices and their capability to access significant private and confidential information have resulted in these devices being targeted by malware developers. Existing Android malware analysis techniques can be broadly categorized into static and dynamic analysis. In this paper, we present two machine learning aided approaches for static analysis of Android malware. The first approach is based on permissions and the other is based on source code analysis utilizing a bag-of-words representation model. Our permission-based model is computationally inexpensive, and is implemented as the feature of OWASP Seraphimdroid Android app that can be obtained from Google Play Store. Our evaluations of both approaches indicate an F-score of 95.1% and F-measure of 89% for the source code-based classification and permission-based classification models, respectively.
16 Aug 2017

Event (Conference)

TitlebSides Manchester 2017
Web address (URL)
LocationManchester Metropolitan University Business School
Country/TerritoryUnited Kingdom
Degree of recognitionLocal event