Biblio

Android malware growth has been increasing dramatically as well as the diversity and complicity of their developing techniques. Machine learning techniques have been applied to detect malware by modeling patterns of static features and dynamic behaviors of malware. The accuracy rates of the machine learning classifiers differ depending on the quality of the features. We increase the quality of the features by relating between the apps' features and the features that are required to deliver its category's functionality. To measure the benign app references, the features of the top rated apps in a specific category are utilized to train a malware detection classifier for that given category. Android apps stores such as Google Play organize apps into different categories. Each category has its distinct functionalities which means the apps under a specific category are similar in their static and dynamic features. In other words, benign apps under a certain category tend to share a common set of features. On the contrary, malicious apps tend to have abnormal features, which are uncommon for the category that they belong to. This paper proposes category-based machine learning classifiers to enhance the performance of classification models at detecting malicious apps under a certain category. The intensive machine learning experiments proved that category-based classifiers report a remarkable higher average performance compared to non-category based.