Mobile malware detection - An analysis of the impact of feature categories

Abstract

The use of smartphones and hand-held devices continues to increase with rapid development in underlying technology and widespread deployment of numerous applications including social network, email and financial transactions. Inevitably, malware attacks are shifting towards these devices. To detect mobile malware, features representing the characteristics of applications play a crucial role. In this work, we systematically studied the impact of all categories of features (i.e., permission, application programmers interface calls, inter component communication and dynamic features) of android applications in classifying a malware from benign applications. We identified the best combination of feature categories that yield better performance in terms of widely used metrics than blindly using all feature categories. We proposed a new technique to include contextual information in API calls into feature values and the study reveals that embedding such information enhances malware detection capability by a good margin. Information gain analysis shows that a significant number of features in ICC category is not relevant to malware prediction and hence, least effective. This study will be useful in designing better mobile malware detection system.