ENSEMBLE-DROID: Optimized Multi-Model Detection of Android Malware

Android platform due to open-source characteristics and Google backing has the largest global market share. Being the world’s most popular operating system, it has drawn the attention of cyber criminals operating particularly through the wide distribution of malicious applications. This paper proposes an effectual machine-learning-based approach for Android Malware Detection making use of an evolutionary chi-square algorithm for discriminatory feature selection. Selected features from the chi-square algorithm are used to train machine learning classifiers and their capability in identification of Malware before and after feature selection is compared. The experimentation results validate that the chi-square algorithm gives the most optimized feature subset helping in the reduction of feature dimension to less than half of the original feature set. Classification accuracy of more than the previous percentage is maintained post-feature selection for the machine learning-based classifiers, while working on much reduced feature dimension, thereby, having a positive impact on the computational complexity of learning classifiers.  Algorithms: Random Forest Classifier Extra Trees Classifier Artificial neural network CNN Android Apps are freely available on Google Play store, the official Android app store as well as third-party app stores for users to download. Due to its open-source nature and popularity, malware writers are increasingly focusing on developing malicious applications for the Android operating system. Despite various attempts by Google Play store to protect against malicious apps, they still find their way to mass market and cause harm to users by misusing personal information related to their phone book, mail accounts, GPS location information, and others for misuse by third parties or else take control of the phones remotely. Therefore, there is a need to perform malware analysis or reverse-engineering of such malicious applications which pose a serious threat to Android Platforms.