An Enhanced Deep Learning Neural Network for the Detection and Identification of Android Malware

Android-based mobile devices have attracted a large number of users because they are easy to use and possess a wide range of capabilities. Because of its popularity, Android has become one of the most important platforms for attackers to launch their nefarious schemes. Due to the rising sophistication of Android malware obfuscation and detection avoidance tactics, many traditional malware detection approaches have become impractical due to their limited representation capabilities. Inspired by the success of deep learning in representation learning, this article presents an effective improved deep neural network to safeguard Android devices from malicious apps called AMDI-Droid. The presented approach contains three enhancements: 1) from the ensemble classifier perspective, we propose a new architecture based on a deep neural network, where the predictive outputs obtained from all hidden layers are blended to produce a final prediction; 2) the first hidden layer learns an effective feature representation from the original data through multiple subnetworks; and 3) a loss function is formulated by combining the predictive loss of each base classifier connected to the corresponding hidden layer. The superior performance of the proposed model is verified via intensive evaluations against state-of-the-art techniques in terms of the accuracy, precision, recall, F1-score, and Matthews correlation coefficient (MCC) metrics.