References of "Wang, Haoyu"
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See detailOn the Impact of Sample Duplication in Machine Learning based Android Malware Detection
Zhao, Yanjie; Li, Li; Wang, Haoyu et al

in ACM Transactions on Software Engineering and Methodology (2021), 30(3), 1-38

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See detailKnowledgezooclient: Constructing knowledge graph for android
Li, Li; Gao, Jun UL; Kong, Pingfan UL et al

in The 3rd International Workshop on Advances in Mobile App Analysis (2020, September)

In this work, we describe the design and implementation of a reusable tool named KnowledgeZooClient targeting the construction, as a crowd-sourced effort, of a knowledge graph for Android apps ... [more ▼]

In this work, we describe the design and implementation of a reusable tool named KnowledgeZooClient targeting the construction, as a crowd-sourced effort, of a knowledge graph for Android apps. KnowledgeZooClient is made up of two modules: (1) the Metadata Extraction Module (MEM), which aims at extracting metadata from Android apps and (2) the Metadata Integration Module (MIM) for importing and integrating extracted metadata into a graph database. The usefulness of KnowledgeZooClient is demonstrated via an exclusive knowledge graph called KnowledgeZoo, which contains information on over 500,000 apps already and still keeps growing. Interested users can already benefit from KnowledgeZoo by writing advanced search queries so as to collect targeted app samples. [less ▲]

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See detailMadDroid: Characterizing and Detecting Devious Ad Contents for Android Apps
Liu, Tianming; Wang, Haoyu; Li, Li et al

in Proceedings of The Web Conference 2020 (2020, April)

Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, which poses a number of ... [more ▼]

Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, which poses a number of threats, e.g., propagating malware and undesirable contents. To understand the practice of these devious ad behaviors, we perform a large-scale study on the app contents harvested through automated app testing. In this work, we first provide a comprehensive categorization of devious ad contents, including five kinds of behaviors belonging to two categories: ad loading content and ad clicking content. Then, we propose MadDroid, a framework for automated detection of devious ad contents. MadDroid leverages an automated app testing framework with a sophisticated ad view exploration strategy for effectively collecting ad-related network traffic and subsequently extracting ad contents. We then integrate dedicated approaches into the framework to identify devious ad contents. We have applied MadDroid to 40,000 Android apps and found that roughly 6% of apps deliver devious ad contents, e.g., distributing malicious apps that cannot be downloaded via traditional app markets. Experiment results indicate that devious ad contents are prevalent, suggesting that our community should invest more effort into the detection and mitigation of devious ads towards building a trustworthy mobile advertising ecosystem. [less ▲]

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See detailRevisiting the impact of common libraries for android-related investigations
Li, Li; Riom, Timothée UL; Bissyande, Tegawendé François D Assise UL et al

in Journal of Systems and Software (2019), 154

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See detailOn Identifying and Explaining Similarities in Android Apps
Li, Li; Bissyande, Tegawendé François D Assise UL; Wang, Haoyu et al

in Journal of Computer Science and Technology (2019), 34(2), 437-455

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See detailFraudDroid: Automated Ad Fraud Detection for Android Apps
Dong, Feng; Wang, Haoyu; Li, Li et al

in ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2018) (2018, November)

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See detailCiD: Automating the Detection of API-related Compatibility Issues in Android Apps
Li, Li; Bissyande, Tegawendé François D Assise UL; Wang, Haoyu et al

in International Symposium on Software Testing and Analysis (ISSTA) (2018, July)

Detailed reference viewed: 183 (3 UL)