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A deep Learning Approach to Malware detection in android platform

Throughout the year 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 accessed a huge range of applications from both commercial and private use. Malware de...

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Bibliographic Details
Main Author: Corrine, Francis
Format: Final Year Project Report
Language:English
English
Published: Universiti Malaysia Sarawak (UNIMAS) 2018
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/29064/1/A%20deep%20Learning%20Approach%20to%20Malware%20detection%20in%20android%20platform%20%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/29064/2/A%20deep%20Learning%20Approach..ft.pdf
http://ir.unimas.my/id/eprint/29064/
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spelling my.unimas.ir.290642023-03-02T04:49:52Z http://ir.unimas.my/id/eprint/29064/ A deep Learning Approach to Malware detection in android platform Corrine, Francis T Technology (General) Throughout the year 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 accessed 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 were discovered. Malware is a common term used to describe malicious software that can induced security threats to any devices and also to the Internet network. In this study, a malware detection that is based on Deep Learning approach that utilize the Long-Short Term Memory Networks (LSTM) model in utilized. The chosen approach will learn and train itself by using the features that are needed for malware detection using a large data sets for evaluating the trained algorithm. The performance of the model is evaluated by comparing it with the Back-Propagation (BP) model. Results that was achieved by conducting the necessary experiments proved that the LSTM model is capable to detect malware with the error loss of 0.6 and achieved an accuracy of 93.60% compared to BP with an accuracy of 82.85%. Universiti Malaysia Sarawak (UNIMAS) 2018 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/29064/1/A%20deep%20Learning%20Approach%20to%20Malware%20detection%20in%20android%20platform%20%2824%20pgs%29.pdf text en http://ir.unimas.my/id/eprint/29064/2/A%20deep%20Learning%20Approach..ft.pdf Corrine, Francis (2018) A deep Learning Approach to Malware detection in android platform. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic T Technology (General)
spellingShingle T Technology (General)
Corrine, Francis
A deep Learning Approach to Malware detection in android platform
description Throughout the year 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 accessed 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 were discovered. Malware is a common term used to describe malicious software that can induced security threats to any devices and also to the Internet network. In this study, a malware detection that is based on Deep Learning approach that utilize the Long-Short Term Memory Networks (LSTM) model in utilized. The chosen approach will learn and train itself by using the features that are needed for malware detection using a large data sets for evaluating the trained algorithm. The performance of the model is evaluated by comparing it with the Back-Propagation (BP) model. Results that was achieved by conducting the necessary experiments proved that the LSTM model is capable to detect malware with the error loss of 0.6 and achieved an accuracy of 93.60% compared to BP with an accuracy of 82.85%.
format Final Year Project Report
author Corrine, Francis
author_facet Corrine, Francis
author_sort Corrine, Francis
title A deep Learning Approach to Malware detection in android platform
title_short A deep Learning Approach to Malware detection in android platform
title_full A deep Learning Approach to Malware detection in android platform
title_fullStr A deep Learning Approach to Malware detection in android platform
title_full_unstemmed A deep Learning Approach to Malware detection in android platform
title_sort deep learning approach to malware detection in android platform
publisher Universiti Malaysia Sarawak (UNIMAS)
publishDate 2018
url http://ir.unimas.my/id/eprint/29064/1/A%20deep%20Learning%20Approach%20to%20Malware%20detection%20in%20android%20platform%20%2824%20pgs%29.pdf
http://ir.unimas.my/id/eprint/29064/2/A%20deep%20Learning%20Approach..ft.pdf
http://ir.unimas.my/id/eprint/29064/
_version_ 1759693311518441472
score 13.211869

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