DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network

Abstract Android smartphones are being utilized by a vast majority of users for everyday planning, data exchanges, correspondences, social interaction, business execution, bank transactions, and almost in each walk of everyday lives. With the expansion of human reliance on smartphone technology, cyberattacks against these devices have surged exponentially. Smartphone applications use permissions to utilize various functionalities of the smartphone that can be maneuvered to launch an attack or inject malware by hackers. Existing studies present various approaches to detect Android malware but lack early detection and identification. Accordingly, there is a dire need to craft an efficient mechanism for malicious applications’ detection before they exploit the data. In this paper, a novel approach DeepAMD to defend against real-world Android malware using deep Artificial Neural Network (ANN) has been adopted including an efficiency comparison of DeepAMD with conventional machine learning classifiers and state-of-the-art studies based on performance measures such as accuracy, recall, f-score, and precision. As per the experimental analysis, DeepAMD outperforms other approaches in detecting and identifying malware attacks on both Static as well as Dynamic layers. On the Static layer, DeepAMD achieves the highest accuracy of 93.4% for malware classification, 92.5% for malware category classification, and 90% for malware family classification. On the Dynamic layer, DeepAMD achieves the highest accuracy of 80.3% for malware category classification and 59% for malware family classification in comparison with the state-of-the-art techniques.