A Deep Learning Approach to Malware Detection in Android Platform
Abdulrazak Yahya Saleh1, Corrine Francis2

1Abdulrazak Yahya Saleh, FSKPM Faculty, University Malaysia Sarawak (UNIMAS), Kota Samarahan, 94300 Sarawak, Malaysia.
2Corrine Francis, FSKPM Faculty, University Malaysia Sarawak (UNIMAS), Kota Samarahan, 94300 Sarawak, Malaysia.

Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 1043-1048 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6562068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Throughout the years, mobile devices such as tablets, smartphones and computers are extremely widespread because of the development of modern technology. By using these devices, users all over the globe can easily access a huge range of applications from both commercial and private use. Malware detection is an important aspect of software protection. As a matter of fact, the development of malware had begun soaring as more and more unknown malware are discovered. Malware is a common term used to describe malicious software that can induce security threats to any device and also to the Internet network. In this paper, malware detection based Deep Learning approach utilizing the Long-Short Term Memory Networks (LSTM) algorithm is conducted by the researchers. The chosen approach learns and trains itself using the features that are needed for malware detection. Then, large data sets are used for evaluating the trained algorithm.
Keyword: Android Platform, Deep Learning, Long-Short Term Memory, Malware, Malware Detection.
Scope of the Article: Web-Based Learning: Innovation and Challenges.