Detection of Android Malware based on Sequence Alignment of Permissions

Permissions control accesses to critical resources on Android. Any weaknesses from their exploitation can be of great interest to attackers. Investigation about associations of permissions can reveal some patterns against attacks. In this regards, this paper proposes an approach based on sequence alignment between requested permissions to identify similarities between applications. Permission patterns for malicious and normal samples are determined and exploited to evaluate a similarity score. The nature of an application is obtained based on a threshold, judiciously computed. Experiments have been realized with a dataset of 534 malicious samples (300 training and 234 testing) and 534 normal samples (300 training and 234 testing). Our approach has been able to recognize testing samples (either malware or normal) with an accuracy of 79%, an average precision of 76% and an average recall of 75%. This research reveals that sequence alignment can improve malware detection research