research-article

A Novel Android Malware Detection Approach Based on Convolutional Neural Network

Authors:

Xiaolei WangAuthors Info & Claims

ICCSP 2018: Proceedings of the 2nd International Conference on Cryptography, Security and Privacy

Pages 144 - 149

https://doi.org/10.1145/3199478.3199492

Published: 16 March 2018 Publication History

Abstract

With the explosive growth of Android malware, there is a pressure for us to improve the performance of existing malware detection approaches. In this paper, we proposed DeepClassifyDroid, a novel android malware detection system based on deep learning. DeepClassifyDroid takes a three-step approach: feature extraction, feature embedding and deep learning based detection. The first and second steps perform a broad static analysis and generate five different feature sets. The last step performs malware detection based on convolutional neural networks. We evaluated our approach with different feature sets and compared with a variety of machine learning based approaches. Study shows that DeepClassifyDroid outperforms most existing machine learning based approaches and detects 97.4% of the malware with few false alarms. Moreover, our approach is 10 times faster than Linear-SVM and 80 times faster than kNN.

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Cited By

Neil AShabaan EEl Qout MEmara K(2024)Machine Learning Based Approaches For Android Malware Detection using Hybrid Feature Analysis2024 6th International Conference on Computing and Informatics (ICCI)10.1109/ICCI61671.2024.10485163(158-165)Online publication date: 6-Mar-2024
https://doi.org/10.1109/ICCI61671.2024.10485163
Chimezie Obidiagha CRahouti MHayajneh T(2024)DeepImageDroid: A Hybrid Framework Leveraging Visual Transformers and Convolutional Neural Networks for Robust Android Malware DetectionIEEE Access10.1109/ACCESS.2024.348559312(156285-156306)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3485593
Wang MZhang YWen W(2024)Improved capsule networks based on Nash equilibrium for malicious code classificationComputers and Security10.1016/j.cose.2023.103503136:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.cose.2023.103503
Show More Cited By

Index Terms

A Novel Android Malware Detection Approach Based on Convolutional Neural Network
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection
    2. Machine learning approaches
      1. Neural networks
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Malware and its mitigation

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cover image ACM Other conferences

ICCSP 2018: Proceedings of the 2nd International Conference on Cryptography, Security and Privacy

March 2018

187 pages

ISBN:9781450363617

DOI:10.1145/3199478

Copyright © 2018 ACM.

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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Wuhan Univ.: Wuhan University, China
University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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Association for Computing Machinery

New York, NY, United States

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Published: 16 March 2018

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ICCSP 2018

ICCSP 2018: 2018 the 2nd International Conference on Cryptography, Security and Privacy

March 16 - 19, 2018

Guiyang, China

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Cited By

Neil AShabaan EEl Qout MEmara K(2024)Machine Learning Based Approaches For Android Malware Detection using Hybrid Feature Analysis2024 6th International Conference on Computing and Informatics (ICCI)10.1109/ICCI61671.2024.10485163(158-165)Online publication date: 6-Mar-2024
https://doi.org/10.1109/ICCI61671.2024.10485163
Chimezie Obidiagha CRahouti MHayajneh T(2024)DeepImageDroid: A Hybrid Framework Leveraging Visual Transformers and Convolutional Neural Networks for Robust Android Malware DetectionIEEE Access10.1109/ACCESS.2024.348559312(156285-156306)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3485593
Wang MZhang YWen W(2024)Improved capsule networks based on Nash equilibrium for malicious code classificationComputers and Security10.1016/j.cose.2023.103503136:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.cose.2023.103503
Siddiqui SKhan T(2024)An Overview of Techniques for Obfuscated Android Malware DetectionSN Computer Science10.1007/s42979-024-02637-35:4Online publication date: 16-Mar-2024
https://doi.org/10.1007/s42979-024-02637-3
Sumra VMalik NKumar S(2024)An Inclusive Analysis on Deep Learning Hinged Malware Detection TechniquesArtificial Intelligence and Speech Technology10.1007/978-3-031-75167-7_33(417-425)Online publication date: 24-Nov-2024
https://doi.org/10.1007/978-3-031-75167-7_33
Dasiah NGain RSabarisrinivas VSitara K(2024)A Deep Learning Method for Obfuscated Android Malware DetectionMachine Learning, Image Processing, Network Security and Data Sciences10.1007/978-3-031-62217-5_13(149-164)Online publication date: 11-Jun-2024
https://doi.org/10.1007/978-3-031-62217-5_13
Ding ADe Alwis CLiyanage M(2024)ReferencesMobile Edge Computing and Communications10.1002/9781119611646.refs(209-243)Online publication date: 27-Dec-2024
https://doi.org/10.1002/9781119611646.refs
Menaouer BIslem ANada M(2023)Android Malware Detection Approach Using Stacked AutoEncoder and Convolutional Neural NetworksInternational Journal of Intelligent Information Technologies10.4018/IJIIT.32995619:1(1-22)Online publication date: 19-Sep-2023
https://dl.acm.org/doi/10.4018/IJIIT.329956
Taher FAl Fandi OAl Kfairy MAl Hamadi HAlrabaee S(2023)A Proposed Artificial Intelligence Model for Android-Malware DetectionInformatics10.3390/informatics1003006710:3(67)Online publication date: 18-Aug-2023
https://doi.org/10.3390/informatics10030067
Jo JCho JMoon J(2023)A Malware Detection and Extraction Method for the Related Information Using the ViT Attention Mechanism on Android Operating SystemApplied Sciences10.3390/app1311683913:11(6839)Online publication date: 5-Jun-2023
https://doi.org/10.3390/app13116839
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