Mitigating Automated Obfuscation Attacks on Software Plagiarism Detection Systems

Plagiarism is a prevalent challenge in computer science education, and educators rely on detection systems to address it at scale. However, these systems remain vulnerable to obfuscation attacks that alter the structure of a program while preserving its behavior. Recent advancements in artificial intelligence have exacerbated this problem. To address these challenges, this dissertation introduces defense mechanisms against automated obfuscation, enhancing the resilience of detection systems.