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Malicious Game Client Detection Using Feature Extraction And Machine Learning, Spencer J. Austad
Malicious Game Client Detection Using Feature Extraction And Machine Learning, Spencer J. Austad
Theses and Dissertations
Minecraft, the world's best-selling video game, boasts a vast and vibrant community of users who actively develop third-party software for the game. However, it has also garnered notoriety as one of the most malware-infested gaming environments. This poses a unique challenge because Minecraft software has many community-specific nuances that make traditional malware analysis less effective. These differences include unique file types, differing code formats, and lack of standardization in user-generated content analysis. This research looks at Minecraft clients in the two most common formats: Portable Executable and Java Archive file formats. Feature correlation matrices showed that malware features are too …