作者
Iram Bibi, Adnan Akhunzada, Jahanzaib Malik, Javed Iqbal, Arslan Musaddiq, Sungwon Kim
发表日期
2020/7/16
期刊
IEEE Access
卷号
8
页码范围
129600-129612
出版商
IEEE
简介
The predominant Android operating system has captured enormous attention globally not only in smart phone industry but also for varied smart devices. The open architecture and application programming interfaces (APIs) while hosting third party applications has led to explosive growth of varied pervasive sophisticated Android malware production. In this study, we propose a robust, scalable and efficient Cuda-empowered multi-class malware detection technique leveraging Gated Recurrent Unit (GRU) to identify sophisticated Android malware. Experimentation of the proposed technique has been carried out using current state-of-the-art datasets of Android applications (i.e., Android Malware Dataset (AMD), Androzoo). Moreover, to rigorously evaluate the performance of the proposed technique, we have employed standard performance evaluation metrics (e.g., accuracy, precision, recall, F1-score etc.) and …
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I Bibi, A Akhunzada, J Malik, J Iqbal, A Musaddiq… - IEEE Access, 2020