research-article

DeUEDroid: Detecting Underground Economy Apps Based on UTG Similarity

Authors:
Zhuo Chen

Zhejiang University, China / Ant Group, China

Zhejiang University, China / Ant Group, China
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,
Jie Liu

Ant Group, China

Ant Group, China
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,
Yubo Hu

Xidian University, China

Xidian University, China
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,
Lei Wu

Zhejiang University, China

Zhejiang University, China
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,
Yajin Zhou

Zhejiang University, China

Zhejiang University, China
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,
Yiling He

Zhejiang University, China

Zhejiang University, China
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,
Xianhao Liao

Ant Group, China

Ant Group, China
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,
Ke Wang

Ant Group, China

Ant Group, China
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,
Jinku Li

Xidian University, China

Xidian University, China
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,
Zhan Qin

Zhejiang University, China

Zhejiang University, China
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ISSTA 2023: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and AnalysisJuly 2023Pages 223–235https://doi.org/10.1145/3597926.3598051

Published:13 July 2023Publication History

Related Artifact: DeUEDroid system July 2023 software https://doi.org/10.5281/zenodo.7962231

ISSTA 2023: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis

Pages 223–235

ABSTRACT

In recent years, the underground economy is proliferating in the mobile system. These underground economy apps (UEware for short) make profits from providing non-compliant services, especially in sensitive areas (e.g., gambling, porn, loan). Unlike traditional malware, most of them (over 80%) do not have malicious payloads. Due to their unique characteristics, existing detection approaches cannot effectively and efficiently mitigate this emerging threat. To address this problem, we propose a novel approach to effectively and efficiently detect UEware by considering their UI transition graphs (UTGs). Based on the proposed approach, we design and implement a system, named DeUEDroid, to perform the detection. To evaluate DeUEDroid, we collect 25, 717 apps and build up the first large-scale ground-truth dataset (1, 700 apps) of UEware. The evaluation result based on the ground-truth dataset shows that DeUEDroid can cover new UI features and statically construct precise UTG. It achieves 98.22% detection F1-score and 98.97% classification accuracy, a significantly better performance than the traditional approaches. The evaluation result involving 24, 017 apps demonstrates the effectiveness and efficiency of UEware detection in real-world scenarios. Furthermore, the result also reveals that UEware are prevalent, i.e., 54% apps in the wild and 11% apps in the app stores are UEware. Our work sheds light on the future work of analyzing and detecting UEware. To engage the community, we have made our prototype system and the dataset available online.

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issta23main-p73-p-archive.zip (829.3 KB)

This is the appendix material of our work accpeted by ISSTA 2023. In this paper, we propose a novel approach to effectively and efficiently detect UEware by considering their UI transition graphs (UTGs). Based on the proposed approach, we design and implement a system, named DeUEDroid, to perform the detection. Our work sheds light on the future work of analyzing and detecting UEware.

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Received 2023-02-16; accepted 2023-05-03 Google Scholar

Index Terms

DeUEDroid: Detecting Underground Economy Apps Based on UTG Similarity
1. Security and privacy
  1. Software and application security
    1. Software security engineering

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ISSTA 2023: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis
July 2023
1554 pages
ISBN:9798400702211
DOI:10.1145/3597926
General Chair:
René Just
University of Washington, USA
,
Program Chair:
Gordon Fraser
University of Passau, Germany
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machine learning
underground economy
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DeUEDroid: Detecting Underground Economy Apps Based on UTG Similarity

ISSTA 2023: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis

ABSTRACT

Supplemental Material

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Cited By

Index Terms

Recommendations

Detecting Android malicious apps and categorizing benign apps with ensemble of classifiers

Network-based detection of Android malicious apps

Detecting money-stealing apps in alternative Android markets