JSidentify-V2: Leveraging Dynamic Memory Fingerprinting for Mini-Game Plagiarism Detection

2025-08-03

AI Summary

Traditional static analysis fails to detect code plagiarism in mini-game platforms due to sophisticated obfuscation techniques—such as dynamic decryption and key-dependent logic—that undermine syntactic and structural similarity. Method: This paper proposes a robust detection framework based on dynamic memory fingerprints. It employs runtime instrumentation to capture stable memory-access behaviors, integrates static pre-analysis with adaptive hot-object slicing, and constructs obfuscation-resilient memory dependency graphs as behavioral fingerprints. Plagiarism across obfuscated variants is then identified via graph similarity analysis. Contribution/Results: The proposed four-stage pipeline is evaluated on a large-scale dataset of 1,200 mini-games. It achieves accurate detection of code reuse under eight prevalent obfuscation strategies, significantly overcoming the limitations of static analysis in highly obfuscated scenarios.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

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 ...

Problem

• Detect mini-game plagiarism using dynamic memory fingerprinting
• Overcome limitations of static analysis in deep obfuscation scenarios
• Identify stable runtime memory patterns despite code distortion

Innovation

• Dynamic memory fingerprinting for plagiarism detection
• Memory Dependency Graph for obfuscation resilience
• Graph-based similarity analysis for code comparison