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A kernel-based monitoring approach for analyzing malicious behavior on Android

Published: 24 March 2014 Publication History

Abstract

This paper proposes a new technique that monitors important events at the kernel level of Android and analyzes malicious behavior systematically. The proposed technique is designed in two ways. First, in order to analyze malicious behavior that might happen inside one application, it monitors file operations by hooking the system calls to create, read from, and write to a file. Secondly, in order to analyze malicious behavior that might happen in the communication between colluding applications, it monitors IPC messages (Intents) by hooking the binder driver. Our technique can detect even the behavior of obfuscated malware using a run-time monitoring method. In addition, it can reduce the possibility of false detection by providing more specific analysis results compared to the existing methods on Android. Experimental results show that our technique is effective to analyze malicious behavior on Android and helpful to detect malware.

References

[1]
Zhou, Y., & Jiang, X. (2012) Dissecting android malware: Characterization and evolution. In Security and Privacy (SP), 2012 IEEE Symposium on (pp. 95--109). IEEE.
[2]
Isohara, T., Takemori, K., Kubota, A. (2011) Kernel-based Behavior Analysis for Android Malware Detection. In Computational Intelligence and Security (CIS), 2011 Seventh International Conference on (pp. 1011--1015). IEEE.
[3]
Enck, W., Gilbert, P., Chun, B. G., Cox, L. P., Jung, J., McDaniel, P., & Sheth, A. N. (2010) TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones. In Proceedings of the 9th USENIX conference on Operating systems design and implementation (pp. 1--6).
[4]
Dietz, M., Shekhar, S., Pisetsky, Y., Shu, A., & Wallach, D. S. (2011) Quire: Lightweight provenance for smart phone operating systems. In Proceedings of the 20th USENIX Security Symposium.
[5]
Zhou, Y. and Jiang, X. Android Malware Genome Project. www.malgenomeproject.org

Cited By

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  • (2023)Bytecode-Based Android Malware Detection Applying Convolutional Neural NetworksInternational Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023)10.1007/978-3-031-42519-6_11(111-121)Online publication date: 27-Aug-2023
  • (2022)CEMDAProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507009(917-925)Online publication date: 25-Apr-2022
  • (2022)Android Malware Classification by CNN-LSTM2022 International Conference on Smart Information Systems and Technologies (SIST)10.1109/SIST54437.2022.9945816(1-4)Online publication date: 28-Apr-2022
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  1. A kernel-based monitoring approach for analyzing malicious behavior on Android

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    cover image ACM Conferences
    SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
    March 2014
    1890 pages
    ISBN:9781450324694
    DOI:10.1145/2554850
    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 March 2014

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    Author Tags

    1. Android malware
    2. kernel-based monitoring
    3. malware detection
    4. monitoring
    5. signature based detection

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    SAC 2014
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    SAC 2014: Symposium on Applied Computing
    March 24 - 28, 2014
    Gyeongju, Republic of Korea

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    SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    The 40th ACM/SIGAPP Symposium on Applied Computing
    March 31 - April 4, 2025
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    Cited By

    View all
    • (2023)Bytecode-Based Android Malware Detection Applying Convolutional Neural NetworksInternational Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023)10.1007/978-3-031-42519-6_11(111-121)Online publication date: 27-Aug-2023
    • (2022)CEMDAProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507009(917-925)Online publication date: 25-Apr-2022
    • (2022)Android Malware Classification by CNN-LSTM2022 International Conference on Smart Information Systems and Technologies (SIST)10.1109/SIST54437.2022.9945816(1-4)Online publication date: 28-Apr-2022
    • (2022)IoT Malware Classification Based on Lightweight Convolutional Neural NetworksIEEE Internet of Things Journal10.1109/JIOT.2021.31000639:5(3770-3783)Online publication date: 1-Mar-2022
    • (2022)Live system call trace reconstruction on LinuxForensic Science International: Digital Investigation10.1016/j.fsidi.2022.30139842(301398)Online publication date: Jul-2022
    • (2022)Data-aware process discovery for malware detection: an empirical studyMachine Learning10.1007/s10994-022-06154-3112:4(1171-1199)Online publication date: 31-Mar-2022
    • (2021)User Identity Hiding Method of AndroidResearch Anthology on Securing Mobile Technologies and Applications10.4018/978-1-7998-8545-0.ch022(413-425)Online publication date: 2021
    • (2021)Malicious application detection in android — A systematic literature reviewComputer Science Review10.1016/j.cosrev.2021.10037340(100373)Online publication date: May-2021
    • (2020)User Identity Hiding Method of AndroidInternational Journal of Digital Crime and Forensics10.4018/IJDCF.202007010212:3(15-26)Online publication date: Jul-2020
    • (2020)Data-Aware Declarative Process Mining for Malware Detection2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9206902(1-8)Online publication date: Jul-2020
    • Show More Cited By

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