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Android Malware Detection Based on Convolutional Neural Networks

Authors:

Qixu LiuAuthors Info & Claims

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

Article No.: 151, Pages 1 - 6

https://doi.org/10.1145/3331453.3361306

Published: 22 October 2019 Publication History

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Abstract

Due to the open source and fragmentation of the Android system, its security is increasingly challenged. Currently, Android malware detection has certain deficiencies in large-scale and automation detection. In this paper, we proposed an Android malware detection framework based on Convolutional Neural Network (CNN). We used static analysis tools and python scripts to automatically extract 1003 static features, and transformed the features of each sample into a two-dimensional matrix as input to the CNN model. We selected 5000 malicious samples and 5000 benign samples for verification. The experimental results show that the detection accuracy of CNN reaches 99.68%, which is much higher than other algorithms.

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Cited By

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Gu WXing HHou T(2024)MFEMDroid: A Novel Malware Detection Framework Using Combined Multitype Features and Ensemble ModelingIET Information Security10.1049/2024/28508042024(1-12)Online publication date: 17-Feb-2024
https://doi.org/10.1049/2024/2850804
Shu LDong SSu HHuang J(2023)Android Malware Detection Methods Based on Convolutional Neural Network: A SurveyIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32818337:5(1330-1350)Online publication date: Oct-2023
https://doi.org/10.1109/TETCI.2023.3281833
Balikcioglu PSirlanci MA. Kucuk OUlukapi BTurkmen RAcarturk C(2022)Malicious code detection in android: the role of sequence characteristics and disassembling methodsInternational Journal of Information Security10.1007/s10207-022-00626-222:1(107-118)Online publication date: 6-Nov-2022
https://doi.org/10.1007/s10207-022-00626-2
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Index Terms

Android Malware Detection Based on Convolutional Neural Networks
1. Security and privacy
  1. Software and application security
    1. Software security engineering

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Published In

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

October 2019

942 pages

ISBN:9781450362948

DOI:10.1145/3331453

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zeshui Xu

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 22 October 2019

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National Key Research and Development Plan
Key Lab of Information Network Security, Ministry of Public Security
Special fund on education and teaching reform of Besti
key laboratory of network assessment technology of Institute of Information Engineering, Chinese Academy of Sciences

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CSAE 2019

CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering

October 22 - 24, 2019

Sanya, China

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Overall Acceptance Rate 368 of 770 submissions, 48%

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Gu WXing HHou T(2024)MFEMDroid: A Novel Malware Detection Framework Using Combined Multitype Features and Ensemble ModelingIET Information Security10.1049/2024/28508042024(1-12)Online publication date: 17-Feb-2024
https://doi.org/10.1049/2024/2850804
Shu LDong SSu HHuang J(2023)Android Malware Detection Methods Based on Convolutional Neural Network: A SurveyIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32818337:5(1330-1350)Online publication date: Oct-2023
https://doi.org/10.1109/TETCI.2023.3281833
Balikcioglu PSirlanci MA. Kucuk OUlukapi BTurkmen RAcarturk C(2022)Malicious code detection in android: the role of sequence characteristics and disassembling methodsInternational Journal of Information Security10.1007/s10207-022-00626-222:1(107-118)Online publication date: 6-Nov-2022
https://doi.org/10.1007/s10207-022-00626-2
Sharma TRattan D(2021)Malicious application detection in android — A systematic literature reviewComputer Science Review10.1016/j.cosrev.2021.10037340(100373)Online publication date: May-2021
https://doi.org/10.1016/j.cosrev.2021.100373
Pan YGe XFang CFan Y(2020)A Systematic Literature Review of Android Malware Detection Using Static AnalysisIEEE Access10.1109/ACCESS.2020.30028428(116363-116379)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3002842

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Deep Android Malware Detection

Deep Neural Networks for Automatic Android Malware Detection

A Novel Android Malware Detection Approach Based on Convolutional Neural Network

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