Towards Deep Learning-Based Approach for Detecting Android Malware

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Author(s): Jarrett Booz (Towson University, Towson, USA), Josh McGiff (Towson University, Towson, USA), William G. Hatcher (Towson University, Towson, USA), Wei Yu (Towson University, Towson, USA), James Nguyen (Towson University, Towson, USA)and Chao Lu (Towson University, Towson, USA)
Copyright: 2021
Pages: 27
Source title: Research Anthology on Artificial Intelligence Applications in Security
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-7705-9.ch096

Keywords: Artificial Intelligence / Computer Science & IT / Cyber and Network Security / Information Science Reference

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Abstract

In this article, the authors implement a deep learning environment and fine-tune parameters to determine the optimal settings for the classification of Android malware from extracted permission data. By determining the optimal settings, the authors demonstrate the potential performance of a deep learning environment for Android malware detection. Specifically, an extensive study is conducted on various hyper-parameters to determine optimal configurations, and then a performance evaluation is carried out on those configurations to compare and maximize detection accuracy in our target networks. The results achieve a detection accuracy of approximately 95%, with an approximate F1 score of 93%. In addition, the evaluation is extended to include other machine learning frameworks, specifically comparing Microsoft Cognitive Toolkit (CNTK) and Theano with TensorFlow. The future needs are discussed in the realm of machine learning for mobile malware detection, including adversarial training, scalability, and the evaluation of additional data and features.

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Towards Deep Learning-Based Approach for Detecting Android Malware

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