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Machine learning based Android malware classification

Published: 24 September 2019 Publication History

Abstract

As growing Android smart-phones, malware threatens smart-phones is also increasing. There are many types of Android malwares. To detect these Android malwares effectively, first, we need to classify Android malwares. In this paper, we build a database storing Android malwares and their types and characteristics. With the database, we propose a machine learning model to classify the malwares. To evaluate the model, we conducted k-fold cross validation. Through the evaluation, our model showed over 85% accuracy in the malware classification.

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Cited By

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  • (2020)Malware Detection in Android Systems with Traditional Machine Learning Models: A Survey2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)10.1109/HORA49412.2020.9152840(1-8)Online publication date: Jun-2020

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  1. Machine learning based Android malware classification

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    cover image ACM Conferences
    RACS '19: Proceedings of the Conference on Research in Adaptive and Convergent Systems
    September 2019
    323 pages
    ISBN:9781450368438
    DOI:10.1145/3338840
    • Conference Chair:
    • Chih-Cheng Hung,
    • General Chair:
    • Qianbin Chen,
    • Program Chairs:
    • Xianzhong Xie,
    • Christian Esposito,
    • Jun Huang,
    • Juw Won Park,
    • Qinghua Zhang
    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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    New York, NY, United States

    Publication History

    Published: 24 September 2019

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    Author Tags

    1. Android mawlare
    2. machine learning
    3. malware classification
    4. malware database

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    RACS '19 Paper Acceptance Rate 56 of 188 submissions, 30%;
    Overall Acceptance Rate 393 of 1,581 submissions, 25%

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    • (2020)Malware Detection in Android Systems with Traditional Machine Learning Models: A Survey2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)10.1109/HORA49412.2020.9152840(1-8)Online publication date: Jun-2020

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