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Effective and Explainable Detection of Android Malware Based on Machine Learning Algorithms

Published: 12 March 2018 Publication History

Abstract

The across the board reception of android devices and their ability to get to critical private and secret data have brought about these devices being focused by malware engineers. Existing android malware analysis techniques categorized into static and dynamic analysis. In this paper, we introduce two machine learning supported methodologies for static analysis of android malware. The First approach based on statically analysis, content is found through probability statistics which reduces the uncertainty of information. Feature extraction were proposed based on the analysis of existing dataset. Our both approaches were used to high-dimension data into low-dimensional data so as to reduce the dimension and the uncertainty of the extracted features. In training phase the complexity was reduced 16.7% of the original time and detect the unknown malware families were improved.

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  • (2024)The revolution and vision of explainable AI for Android malware detection and protectionInternet of Things10.1016/j.iot.2024.10132027(101320)Online publication date: Oct-2024
  • (2024)MAlign: Explainable static raw-byte based malware family classification using sequence alignmentComputers & Security10.1016/j.cose.2024.103714(103714)Online publication date: Jan-2024
  • (2023)Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive ReviewEnergies10.3390/en1612457316:12(4573)Online publication date: 7-Jun-2023
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  1. Effective and Explainable Detection of Android Malware Based on Machine Learning Algorithms

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    cover image ACM Other conferences
    ICCAI '18: Proceedings of the 2018 International Conference on Computing and Artificial Intelligence
    March 2018
    156 pages
    ISBN:9781450364195
    DOI:10.1145/3194452
    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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    Published: 12 March 2018

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    Author Tags

    1. Android Malware
    2. SVM
    3. dimensional
    4. feature extraction
    5. probability statistics

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    View all
    • (2024)The revolution and vision of explainable AI for Android malware detection and protectionInternet of Things10.1016/j.iot.2024.10132027(101320)Online publication date: Oct-2024
    • (2024)MAlign: Explainable static raw-byte based malware family classification using sequence alignmentComputers & Security10.1016/j.cose.2024.103714(103714)Online publication date: Jan-2024
    • (2023)Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive ReviewEnergies10.3390/en1612457316:12(4573)Online publication date: 7-Jun-2023
    • (2023)GAResNet: A Transfer Learning based Framework for Android Malware Detection2023 IEEE International Conference on Knowledge Graph (ICKG)10.1109/ICKG59574.2023.00038(263-268)Online publication date: 1-Dec-2023
    • (2023)Mal2GCN: a robust malware detection approach using deep graph convolutional networks with non-negative weightsJournal of Computer Virology and Hacking Techniques10.1007/s11416-023-00498-7Online publication date: 27-Sep-2023
    • (2022)Towards Obfuscation Resilient Feature Design for Android Malware Detection-KTSODroidElectronics10.3390/electronics1124407911:24(4079)Online publication date: 8-Dec-2022
    • (2022)Explainable Artificial Intelligence in CyberSecurity: A SurveyIEEE Access10.1109/ACCESS.2022.320417110(93575-93600)Online publication date: 2022
    • (2022)Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research DirectionsMobile Networks and Applications10.1007/s11036-022-01937-328:1(296-312)Online publication date: 14-Mar-2022
    • (2021)A Semi-Automated Explainability-Driven Approach for Malware Analysis through Deep Learning2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9533803(1-8)Online publication date: 18-Jul-2021
    • (2021)Malicious application detection in android — A systematic literature reviewComputer Science Review10.1016/j.cosrev.2021.10037340(100373)Online publication date: May-2021
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