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Deep Neural Network-Based Android Malware Detection (D-AMD)

Deep Neural Network-Based Android Malware Detection (D-AMD)

Sangeetha D., Umamaheswari S., Rakshana Gopalakrishnan
Copyright: © 2021 |Pages: 15
ISBN13: 9781799821083|ISBN10: 1799821080|ISBN13 Softcover: 9781799821090|EISBN13: 9781799821106
DOI: 10.4018/978-1-7998-2108-3.ch006
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MLA

D., Sangeetha, et al. "Deep Neural Network-Based Android Malware Detection (D-AMD)." Deep Learning Applications and Intelligent Decision Making in Engineering, edited by Karthikrajan Senthilnathan, et al., IGI Global, 2021, pp. 161-175. https://doi.org/10.4018/978-1-7998-2108-3.ch006

APA

D., S., S., U., & Gopalakrishnan, R. (2021). Deep Neural Network-Based Android Malware Detection (D-AMD). In K. Senthilnathan, B. Shanmugam, D. Goyal, I. Annapoorani, & R. Samikannu (Eds.), Deep Learning Applications and Intelligent Decision Making in Engineering (pp. 161-175). IGI Global. https://doi.org/10.4018/978-1-7998-2108-3.ch006

Chicago

D., Sangeetha, Umamaheswari S., and Rakshana Gopalakrishnan. "Deep Neural Network-Based Android Malware Detection (D-AMD)." In Deep Learning Applications and Intelligent Decision Making in Engineering, edited by Karthikrajan Senthilnathan, et al., 161-175. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-2108-3.ch006

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Abstract

Android is an operating system that presently has over one billion active users for their mobile devices in which a copious quantity of information is available. Mobile malware causes security incidents like monetary damages, stealing of personal information, etc., when it's deep-rooted into the target devices. Since static and dynamic analysis of Android applications to detect the presence of malware involves a large amount of data, deep neural network is used for the detection. Along with the introduction of batch normalization, the deep neural network becomes effective, and also the time taken by the training process is less. Probabilistic neural network (PNN), convolutional neural network (CNN), and recurrent neural network (RNN) are also used for performance analysis and comparison. Deep neural network with batch normalization gives the highest accuracy of 94.35%.

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