Harnessing Broadcast Receivers for Classification of Android Malware Threats

Abstract

With the increasing number of malicious attacks, the way how to detect and classify malicious apps has drawn attention in mobile technology market. In this paper, we proposed a classification model to seek and track malware Apps broadcast receivers in such devices. To identify the family of apps, static features of each app was extracted and a novel deterministic classifier is employed to categorize malware apps. With such, we can act against malware of known family, since we understand its functions, and prevent it from spreading out in larger scale, affecting extensively our society. Detailed description of the classification model is provided, as well the core technologies of this novel malicious android applications’ model are presented. From experiments performed on a set of Android-based malware apps, we observe that the proposed classification model achieves highest accuracy, true-positive rate, false-positive rate, precision, recall, f-measure in comparison to other methods implemented in published experiments. The proposed classification model is promising since the average accuracy reaches an average of 97.31% and can effectively be applied to Android malware categorization, providing early detection of the capabilities of malware and the prospect of warning users of threatens ahead.