Risk Score Computation for Android Mobile Applications Using the Twin k-NN Approach

The Android operating system has a dominant market for use within a wide range of devices. Along with the widespread growth of the use of the Android system and the development of a huge number of apps for this operating system, new malicious apps are released daily by adversaries, which are difficult to identify and deal with. This is due to them using sophisticated techniques and strikes. Although there are a diverse range of classification models and risk estimation metrics for identifying malware in this operating system, there is still a requirement for more effective approaches in this context. In this paper, we present a new algorithm to calculate the security risk score of Android apps, which can be used to identify malicious apps from benign ones. This algorithm uses a novel technique named twin $k$-nearest neighbor. In this technique, to estimate the security risk of an unknown app, its nearest neighbors to malicious apps and its nearest neighbors to normal apps are computed separately using an appropriate distance formula. Then, the security risk of the input app can be computed using a simple formulation. In this formulation, the average distances of both $k$-nearest malicious apps and $k$-nearest non-malicious apps to the input app are used. In this way, the proposed method can calculate a high security risk for malware and a lower security risk for goodware. Experimental evaluations on real datasets show that the proposed algorithm has better performance over the previously proposed ones in terms of detection rate, precision, recall, and f1-score.