Affiliations: [a] Faculty of Information Technology, Beijing University of Technology, Beijing, China | [b] Department of Computer Science, Lasbela University of Agriculture Water and Marine Sciences, Lasebla, Pakistan | [c] Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia | [d] Faculty of Information Sciences, University of Education, Vehari, Pakistan
Correspondence:
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Corresponding author: Ahsan Wajahat, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China. E-mail: ahsan.sunny56@yahoo.com.
Abstract: With the rise in the use of Android smartphones, there has been a proportional surge in the proliferation of malicious applications (apps). As mobile phone users are at a heightened risk of data theft, detecting malware on Android devices has emerged as a pressing concern within the realm of cybersecurity. Conventional techniques, such as signature-based routines, are no longer sufficient to safeguard users from the continually evolving sophistication and swift behavioral modifications of novel varieties of Android malware. Hence, there has been a significant drive in recent times towards leveraging machine learning (ML) models and methodologies to identify and generalize malicious behavioral patterns of mobile apps for detecting malware. This paper proposes Deep learning (DL) based on new and highly reliable classifier, deep neural decision forest (DNDF) for detecting Android malware. Two datasets were used: Drebin and 2014 for comparison with previous studies, and TUANDROMD collected in 2021 for detecting the latest threats with advanced obfuscation and morphing techniques. We have also calculated the time-consuming and computational resources taken by our classifier. After conducting a thorough performance evaluation, our proposed approach attained impressive results on two datasets. The empirical findings reveal that the proposed DBN and DNDF models demonstrated exceptional performance, achieving an accuracy of 99%, a sensitivity of 1, and an AUC value of 0.98%. The metrics we obtained are comparable to those of state-of-the-art ML-based Android malware detection techniques and several commercial antivirus engines.
Keywords: Android malware detection, deep learning, auto-encoder, deep belief neural network, deep neural decision forest
