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MalRadar: Demystifying Android Malware in the New Era

Authors:

Xuanzhe LiuAuthors Info & Claims

Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 6, Issue 2

Article No.: 40, Pages 1 - 27

https://doi.org/10.1145/3530906

Published: 06 June 2022 Publication History

Abstract

Mobile malware detection has attracted massive research effort in our community. A reliable and up-to-date malware dataset is critical to evaluate the effectiveness of malware detection approaches. Essentially, the malware ground truth should be manually verified by security experts, and their malicious behaviors should be carefully labelled. Although there are several widely-used malware benchmarks in our community (e.g., MalGenome, Drebin, Piggybacking and AMD, etc.), these benchmarks face several limitations including out-of-date, size, coverage, and reliability issues, etc. In this paper, we first make efforts to create MalRadar, a growing and up-to-date Android malware dataset using the most reliable way, i.e., by collecting malware based on the analysis reports of security experts. We have crawled all the mobile security related reports released by ten leading security companies, and used an automated approach to extract and label the useful ones describing new Android malware and containing Indicators of Compromise (IoC) information. We have successfully compiled MalRadar, a dataset that contains 4,534 unique Android malware samples (including both apks and metadata) released from 2014 to April 2021 by the time of this paper, all of which were manually verified by security experts with detailed behavior analysis. Then we characterize the MalRadar dataset from malware distribution channels, app installation methods, malware activation, malicious behaviors and anti-analysis techniques. We further investigate the malware evolution over the last decade. At last, we measure the effectiveness of commercial anti-virus engines and malware detection techniques on detecting malware in MalRadar. Our dataset can be served as the representative Android malware benchmark in the new era, and our observations can positively contribute to the community and boost a series of research studies on mobile security.

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Index Terms

MalRadar: Demystifying Android Malware in the New Era
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Malware and its mitigation

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proceedings of the ACM on Measurement and Analysis of Computing Systems Volume 6, Issue 2

POMACS

June 2022

499 pages

EISSN:2476-1249

DOI:10.1145/3543145

Editors:
Augustin Chaintreau
Columbia University
,
Leana Golubchik
University of Southern California
,
Zhi-Li Zhang
University of Minnesota

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Copyright © 2022 ACM.

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Published: 06 June 2022

Published in POMACS Volume 6, Issue 2

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Rifat MAsha AJadon SYan XGuha SAhmed S(2024)Combating Islamophobia: Compromise, Community, and Harmony in Mitigating Harmful Online ContentACM Transactions on Social Computing10.1145/36415107:1(1-32)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3641510
Campion MDalla Preda MGiacobazzi RUrban C(2024)Monotonicity and the Precision of Program AnalysisProceedings of the ACM on Programming Languages10.1145/36328978:POPL(1629-1662)Online publication date: 5-Jan-2024
https://dl.acm.org/doi/10.1145/3632897
Liu HLiu XXie XTong XLi K(2024)PmTrackProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314337:4(1-30)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631433
Liu HGong LMo XDong GYu J(2024)LTAChecker: Lightweight Android Malware Detection Based on Dalvik Opcode Sequences Using Attention Temporal NetworksIEEE Internet of Things Journal10.1109/JIOT.2024.339455511:14(25371-25381)Online publication date: 15-Jul-2024
https://doi.org/10.1109/JIOT.2024.3394555
Jeremiah SChen HGritzalis SPark J(2024)Leveraging application permissions and network traffic attributes for Android ransomware detectionJournal of Network and Computer Applications10.1016/j.jnca.2024.103950230:COnline publication date: 18-Oct-2024
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Nawshin FGad RUnal DAl-Ali ASuganthan P(2024)Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive surveyComputers and Electrical Engineering10.1016/j.compeleceng.2024.109233117(109233)Online publication date: Jul-2024
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Wang CWang WYao PShi QZhou JXiao XZhang C(2023) Anchor: Fast and Precise Value-flow Analysis for Containers via Memory OrientationACM Transactions on Software Engineering and Methodology10.1145/356580032:3(1-39)Online publication date: 26-Apr-2023
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Liu KZhang GChen XLiu QPeng LYurui L(2023)Android malware detection based on sensitive patternsTelecommunications Systems10.1007/s11235-022-00983-282:4(435-449)Online publication date: 20-Feb-2023
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