research-article

NADM: Neural Network for Android Detection Malware

Authors:

Nguyen Viet Duc,

Pham Thanh GiangAuthors Info & Claims

SoICT '18: Proceedings of the 9th International Symposium on Information and Communication Technology

Pages 449 - 455

https://doi.org/10.1145/3287921.3287977

Published: 06 December 2018 Publication History

Abstract

Over recent years, Android is always captured roughly 80% of the worldwide smartphone volume. Due to its popularity and open characteristic, the Android OS is becoming the system platform most targeted from mobile malware. They can cause a lot of damage on Android devices such as data loss or sabotage of hardware. According to the predictive characteristics, machine learning is a good approach to deal with the number of new malwares increasing rapidly. In this paper, we propose Neural Network for Android Detection of Malware (NADM). The NADM performs an analysis process to gather features of Android applications. Then, these data will be converted into joint vector spaces, which to be input for the training part of deep learning process. Our classifier model can achieve a high accuracy system and has been applied in sProtect [15] on Google Play.

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Cited By

Karthikeyan DSivakumar AArumugam C(2022)Android X-Ray - A system for Malware Detection in Android apps using Dynamic AnalysisWSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS10.37394/23209.2022.19.2719(264-271)Online publication date: 7-Nov-2022
https://doi.org/10.37394/23209.2022.19.27
Sharma RAgrawal CKumar VMulatu A(2022)CFSBFDroidMobile Information Systems10.1155/2022/64255832022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6425583
Liu LRen WXie FYi SYi JJia P(2021)Learning-Based Detection for Malicious Android Application Using Code VectorizationSecurity and Communication Networks10.1155/2021/99642242021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/9964224
Show More Cited By

Index Terms

NADM: Neural Network for Android Detection Malware
1. Security and privacy
  1. Software and application security
    1. Software security engineering

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cover image ACM Other conferences

SoICT '18: Proceedings of the 9th International Symposium on Information and Communication Technology

December 2018

496 pages

ISBN:9781450365390

DOI:10.1145/3287921

Copyright © 2018 ACM.

© 2018 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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SOICT: School of Information and Communication Technology - HUST
NAFOSTED: The National Foundation for Science and Technology Development

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 December 2018

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SoICT 2018

SoICT 2018: The Ninth International Symposium on Information and Communication Technology

December 6 - 7, 2018

Danang City, Viet Nam

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Overall Acceptance Rate 147 of 318 submissions, 46%

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Cited By

Karthikeyan DSivakumar AArumugam C(2022)Android X-Ray - A system for Malware Detection in Android apps using Dynamic AnalysisWSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS10.37394/23209.2022.19.2719(264-271)Online publication date: 7-Nov-2022
https://doi.org/10.37394/23209.2022.19.27
Sharma RAgrawal CKumar VMulatu A(2022)CFSBFDroidMobile Information Systems10.1155/2022/64255832022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6425583
Liu LRen WXie FYi SYi JJia P(2021)Learning-Based Detection for Malicious Android Application Using Code VectorizationSecurity and Communication Networks10.1155/2021/99642242021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/9964224
Liu ZWang RJapkowicz NTang DZhang WZhao J(2021)Research on unsupervised feature learning for Android malware detection based on Restricted Boltzmann MachinesFuture Generation Computer Systems10.1016/j.future.2021.02.015120(91-108)Online publication date: Jul-2021
https://doi.org/10.1016/j.future.2021.02.015
Shabir ASabahat N(2020)A Review of Hybrid Malware Detection Techniques in Android2020 IEEE 23rd International Multitopic Conference (INMIC)10.1109/INMIC50486.2020.9318117(1-6)Online publication date: 5-Nov-2020
https://doi.org/10.1109/INMIC50486.2020.9318117
Wang WZhao MGao ZXu GXian HLi YZhang X(2019)Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and DirectionsIEEE Access10.1109/ACCESS.2019.29181397(67602-67631)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2918139

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