Binary Code Similarity Detection (BCSD) has become a pivotal area of research in cybersecurity, addressing critical applications such as vulnerability detection, malware analysis, and software plagiarism. This review synthesizes recent advancements in BCSD, highlighting the integration of machine learning techniques, the importance of semantic understanding, and the incorporation of domain knowledge. Key challenges, including semantic equivalence, representation of control flow, and scalability, are discussed alongside innovative methodologies that enhance detection accuracy. The review also explores the implications of recent studies on practical applications, including vulnerability search systems and malicious code detection, while identifying future research directions aimed at improving model interpretability, standardizing benchmarks, and addressing the complexities of diverse hardware architectures.
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