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

Deep Neural Network-Based Android Malware Detection (D-AMD)
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Author(s): Sangeetha D. (Anna University, MIT Campus, India), Umamaheswari S. (Anna University, MIT Campus, India) and Rakshana Gopalakrishnan (Anna University, MIT Campus, India)
Copyright: 2021
Pages: 15
Source title: Deep Learning Applications and Intelligent Decision Making in Engineering
Source Author(s)/Editor(s): Karthikrajan Senthilnathan (Revoltaxe India Pvt Ltd, Chennai, India), Balamurugan Shanmugam (Quants IS & CS, India), Dinesh Goyal (Poornima Institute of Engineering and Technology, India), Iyswarya Annapoorani (VIT University, India) and Ravi Samikannu (Botswana International University of Science and Technology, Botswana)
DOI: 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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