People, businesses, and vital infrastructure are at serious danger from the sophisticated and persistent cyberthreats that have emerged as a result of the quickly changing digital world. Modern attack vectors including ransomware, polymorphic malware, advanced persistent threats (APTs), and zero-day vulnerabilities are outperforming traditional cybersecurity systems, which mostly depend on predetermined rules and signature-based detection. As a result, machine learning (ML) and artificial intelligence (AI) have become game-changing technologies that may improve cyber defences via predictive analytics, intelligent automation, and flexibility. The integration of AI and ML into cybersecurity frameworks is examined in this paper, with a focus on how these technologies might improve threat prevention, detection, and response capabilities. Predictive threat modelling, which uses historical and real-time data to predict possible breaches; behavior-based anomaly detection, which spots suspicious activity outside of known attack patterns; automated incident response, which allows for quick threat containment and remediation; and proactive risk assessment, which aids in well-informed security policy decisions, are some of the main application areas. In addition to discussing cutting-edge techniques and technologies now in use, the article offers a systematic analysis of current issues, including model explainability, data quality issues, and adversarial assaults on AI systems.
Srinivas Rao (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: