Title: Analysis of Android Malware Detection Techniques in Deep Learning

Volume 10 Issue 2 Year 2020


Neetu Agarwal

Department of Computer Science & Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan Jaipur-302017 (INDIA),

Vipin Jain

Department of Information Technology, Swami Keshvanand Institute of Technology, Management & Gramothan Jaipur-302017 (INDIA),

Raju K Ranjan

Department ofComputer Science & Engineering, Delhi Technological University, New Delhi-110042 (INDIA)

Pages: 21-26


Over the years, developments in smart phone technology has boost up their use among users. This has captivated malware authors’ attention. Malware attacks in various forms has troubled users by stealing their personal information, banking information and much more. Android users have been strained most because of Android’s open nature. Throughout this time, efforts have been made to devise software and methods to detect android malwares. Starting from anti-virus software to Machine Learning and now Deep Learning, researchers have put forward various techniques to get to grips with the problem. Many Deep Learning Techniques have been put forward like Deep Neural Network, Convolutional Neural Network, Recurrent Neural Network, Deep Belief Network and Autoencoders. This paper looks at and analyzes supervised Deep Learning classifiers to detect Android malware.

Deep Learning, Android Malware, CNN, Neural Network