research-article

Target: category-based android malware detection revisited

Authors:

Hui LiAuthors Info & Claims

ACSW '17: Proceedings of the Australasian Computer Science Week Multiconference

Article No.: 74, Pages 1 - 9

https://doi.org/10.1145/3014812.3014888

Published: 31 January 2017 Publication History

Abstract

Smartphones are becoming increasingly popular in daily routines around the world. However, malware in smartphones is getting more prevalent, and will introduce potential risks to smartphone users. In this paper, we propose a new system, called Target, for detecting malware in the Android operating system, featuring both static and dynamic analysis. Our static analysis is based on user permissions, signatures and source code, and our dynamic analysis is based on the behavior of running mobile applications. A highlight of Target is its ability to reduce the probability of false positives based on the category of applications. Target first generates risk values of the mobile application being analyzed, indicating the degree of risks involved. It then uses a machine learning algorithm, named OKNN, to determine which class an application belongs to. Compared to previous work, Target is able to achieve a significant improvement in terms of malware detection accuracy.

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Cited By

Yunmar RKusumawardani SWidyawan Mohsen F(2024)Hybrid Android Malware Detection: A Review of Heuristic-Based ApproachIEEE Access10.1109/ACCESS.2024.337765812(41255-41286)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3377658
Bilal MRasool GHashmi SMushtaq Z(2023)Pragmatic Evidence on Android Malware Analysis Techniques: A Systematic Literature ReviewInternational Journal of Innovations in Science and Technology10.33411/IJIST/2023050101(1-19)Online publication date: 1-Jan-2023
https://doi.org/10.33411/IJIST/2023050101
Asif MAsif SMubarik IHussain R(2022)Malicious Applications Detection in Android Using Machine LearningInternational Journal of Artificial Intelligence and Machine Learning10.51483/IJAIML.2.2.2022.21-342:2(21-34)Online publication date: 5-Jul-2022
https://doi.org/10.51483/IJAIML.2.2.2022.21-34
Show More Cited By

Index Terms

Target: category-based android malware detection revisited
1. Computing methodologies
  1. Machine learning
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

ACSW '17: Proceedings of the Australasian Computer Science Week Multiconference

January 2017

615 pages

ISBN:9781450347686

DOI:10.1145/3014812

Copyright © 2017 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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Publication History

Published: 31 January 2017

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ACSW 2017

ACSW 2017: Australasian Computer Science Week 2017

January 30 - February 3, 2017

Geelong, Australia

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ACSW '17 Paper Acceptance Rate 78 of 156 submissions, 50%;

Overall Acceptance Rate 204 of 424 submissions, 48%

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Cited By

Yunmar RKusumawardani SWidyawan Mohsen F(2024)Hybrid Android Malware Detection: A Review of Heuristic-Based ApproachIEEE Access10.1109/ACCESS.2024.337765812(41255-41286)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3377658
Bilal MRasool GHashmi SMushtaq Z(2023)Pragmatic Evidence on Android Malware Analysis Techniques: A Systematic Literature ReviewInternational Journal of Innovations in Science and Technology10.33411/IJIST/2023050101(1-19)Online publication date: 1-Jan-2023
https://doi.org/10.33411/IJIST/2023050101
Asif MAsif SMubarik IHussain R(2022)Malicious Applications Detection in Android Using Machine LearningInternational Journal of Artificial Intelligence and Machine Learning10.51483/IJAIML.2.2.2022.21-342:2(21-34)Online publication date: 5-Jul-2022
https://doi.org/10.51483/IJAIML.2.2.2022.21-34
Namrud ZKpodjedo SBali ATalhi C(2022)Deep-Layer Clustering to Identify Permission Usage Patterns of Android App CategoriesIEEE Access10.1109/ACCESS.2022.315608310(24240-24254)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3156083
Moodi MGhazvini MMoodi H(2021)A hybrid intelligent approach to detect Android Botnet using Smart Self-Adaptive Learning-based PSO-SVMKnowledge-Based Systems10.1016/j.knosys.2021.106988222(106988)Online publication date: Jun-2021
https://doi.org/10.1016/j.knosys.2021.106988
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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