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VR-Themis: A Scalable Framework for Virtual Reality Application Clone Detection

arXiv Security Archived Aug 14, 2026 ✓ Full text saved

arXiv:2608.13290v1 Announce Type: new Abstract: Repackaging of mobile applications (aka app cloning) not only threatens the security and privacy of mobile users but also infringes upon the copyright of the original app developers. However, existing detection methods that primarily focus on mobile platforms (such as Android) fail to capture the essential features of virtual reality (VR). Consequently, they are inadequate for effectively detecting cloned VR apps, which have often been targeted by

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    Computer Science > Cryptography and Security [Submitted on 13 Aug 2026] VR-Themis: A Scalable Framework for Virtual Reality Application Clone Detection Gengyang Xu, Hanyang Guo, Hong-Ning Dai, Weizhi Meng Repackaging of mobile applications (aka app cloning) not only threatens the security and privacy of mobile users but also infringes upon the copyright of the original app developers. However, existing detection methods that primarily focus on mobile platforms (such as Android) fail to capture the essential features of virtual reality (VR). Consequently, they are inadequate for effectively detecting cloned VR apps, which have often been targeted by illegal users in the VR market. Considering the unique features of VR apps, this paper proposes a two-stage app clone detection framework, namely VR-Themis, based on \emph{Hierarchy-Object-Behaviour} (HOB). Firstly, VR-Themis exploits the coarse-grained stage to cluster apps based on their retrievable statistical features, making this tool scalable to large-scale VR app datasets. Then, in the fine-grained stage, VR-Themis performs in-depth analysis of the suspicious apps (identified in the first stage) by calculating similarity using our defined \emph{HOB metrics}. Our extensive experiments indicate that VR-Themis successfully detects 307 suspected clone apps from the collected 4,277 VR apps without false positives, demonstrating its effectiveness and scalability. Comments: 25 pages, 14 figures, 1 table. Accepted at the 29th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2026) Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2608.13290 [cs.CR]   (or arXiv:2608.13290v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.13290 Focus to learn more Submission history From: Gengyang Xu [view email] [v1] Thu, 13 Aug 2026 14:24:28 UTC (1,303 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
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    ◬ AI & Machine Learning
    Published
    Aug 14, 2026
    Archived
    Aug 14, 2026
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