JSidentify-V2: Leveraging Dynamic Memory Fingerprinting for Mini-Game Plagiarism Detection
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913972802813952 |
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| author | Li, Zhihao Wang, Chaozheng Li, Zongjie Peng, Xinyong Xia, Qun Lu, Haochuan Xiong, Ting Gao, Shuzheng Gao, Cuiyun Wang, Shuai Deng, Yuetang Ma, Huafeng |
| author_facet | Li, Zhihao Wang, Chaozheng Li, Zongjie Peng, Xinyong Xia, Qun Lu, Haochuan Xiong, Ting Gao, Shuzheng Gao, Cuiyun Wang, Shuai Deng, Yuetang Ma, Huafeng |
| contents | The explosive growth of mini-game platforms has led to widespread code plagiarism, where malicious users access popular games' source code and republish them with modifications. While existing static analysis tools can detect simple obfuscation techniques like variable renaming and dead code injection, they fail against sophisticated deep obfuscation methods such as encrypted code with local or cloud-based decryption keys that completely destroy code structure and render traditional Abstract Syntax Tree analysis ineffective. To address these challenges, we present JSidentify-V2, a novel dynamic analysis framework that detects mini-game plagiarism by capturing memory invariants during program execution. Our key insight is that while obfuscation can severely distort static code characteristics, runtime memory behavior patterns remain relatively stable. JSidentify-V2 employs a four-stage pipeline: (1) static pre-analysis and instrumentation to identify potential memory invariants, (2) adaptive hot object slicing to maximize execution coverage of critical code segments, (3) Memory Dependency Graph construction to represent behavioral fingerprints resilient to obfuscation, and (4) graph-based similarity analysis for plagiarism detection.
We evaluate JSidentify-V2 against eight obfuscation methods on a comprehensive dataset of 1,200 mini-games ... |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01655 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | JSidentify-V2: Leveraging Dynamic Memory Fingerprinting for Mini-Game Plagiarism Detection Li, Zhihao Wang, Chaozheng Li, Zongjie Peng, Xinyong Xia, Qun Lu, Haochuan Xiong, Ting Gao, Shuzheng Gao, Cuiyun Wang, Shuai Deng, Yuetang Ma, Huafeng Cryptography and Security Software Engineering The explosive growth of mini-game platforms has led to widespread code plagiarism, where malicious users access popular games' source code and republish them with modifications. While existing static analysis tools can detect simple obfuscation techniques like variable renaming and dead code injection, they fail against sophisticated deep obfuscation methods such as encrypted code with local or cloud-based decryption keys that completely destroy code structure and render traditional Abstract Syntax Tree analysis ineffective. To address these challenges, we present JSidentify-V2, a novel dynamic analysis framework that detects mini-game plagiarism by capturing memory invariants during program execution. Our key insight is that while obfuscation can severely distort static code characteristics, runtime memory behavior patterns remain relatively stable. JSidentify-V2 employs a four-stage pipeline: (1) static pre-analysis and instrumentation to identify potential memory invariants, (2) adaptive hot object slicing to maximize execution coverage of critical code segments, (3) Memory Dependency Graph construction to represent behavioral fingerprints resilient to obfuscation, and (4) graph-based similarity analysis for plagiarism detection. We evaluate JSidentify-V2 against eight obfuscation methods on a comprehensive dataset of 1,200 mini-games ... |
| title | JSidentify-V2: Leveraging Dynamic Memory Fingerprinting for Mini-Game Plagiarism Detection |
| topic | Cryptography and Security Software Engineering |
| url | https://arxiv.org/abs/2508.01655 |