Optimized Static and Dynamic Android Malware Analysis Using Ensemble Learning

Abstract

As the dominant operating system for mobile devices, Android is the prime target of malicious attackers. Installed Android applications provide an opportunity for attackers to bypass the system’s security. Therefore, it is vital to study and evaluate Android applications to effectively identify harmful applications. Android applications are analyzed by conventional methods using signature hash-based algorithms or static features-based machine learning approaches. This research proposes optimized ensemble classification models for Android applications. Ensemble models have been trained for both static and dynamic analysis using seven and eight distinct classifiers respectively. These models have been optimized by tuning their hyper-parameters and evaluated using K-fold cross-validation. We were able to acquire an F1 score of 99.27% and an accuracy of 99.47% for static analysis and our dynamic analysis model yielded an F1 score of 96.96% and an accuracy of 96.66%. Our proposed approach overcomes conventional solutions by taking into account both static and dynamic analysis and attaining high accuracy with the help of ensemble models.