Is Attention All Security Domain Needs? A Systematic Review of Self-attention Mechanism in Cybersecurity

Self-attention mechanism (SAM) has demonstrated strong analytical power in fields like computer vision, natural language processing, and genomics, but its use within the domain of cybersecurity is exploratory. A growing body of research highlights SAM's applications in critical cybersecurity areas, including intrusion detection, malware analysis, adversarial defense, privacy protection, and data security. Despite these advancements, the current literature largely focus on isolated aspects of SAM's performance, such as model robustness and data confidentiality, while overlooking comprehensive evaluations of its inherent limitations and vulnerabilities. This paper reviews impactful studies employing Security Assurance Metrics (SAM) within security contexts, critically analyzing emerging research trends, relevant methodologies, and case studies. Lastly, it addresses key challenges and proposes directions for future research aimed at advancing SAM's contributions to the evolving landscape of cybersecurity.