<![CDATA[The exponential growth of digital systems has introduced significant cybersecurity challenges, exposing vulnerabilities to increasingly sophisticated threats. Traditional security measures, which rely on static and signature-based methods, often fail to adapt to the dynamic nature of cyberattacks, highlighting the need for innovative solutions. This study aims to develop and evaluate adaptive algorithms in predictive cybersecurity, leveraging Artificial Intelligence (AI) to combat emerging threats such as zero-day exploits and advanced persistent threats (APTs). A simulation-based research design was employed, integrating reinforcement learning frameworks like Deep Q-Learning and utilizing datasets such as CICIDS2017 and synthetic data for zero-day threat simulations. The results show that adaptive algorithms achieved 94.8% detection accuracy, reduced false positives by 54.5%, and improved response times by 53.1%, significantly outper forming static models. Additionally, the adaptive systems demonstrated superiorcapacity to identify novel threats in simulated attack scenarios. These findings underscore the potential of adaptive AI algorithms to revolutionize predictive cybersecurity by offering dynamic, real-time responses to evolving threats. Despite their computational demands posing challenges for smaller organizations, integrating techniques such as adversarial training and robust anomaly detection can enhance resilience. That adaptive algorithms can enhance the resilience and reliability of cybersecurity systems, advocating for future integration with technologies like blockchain and edge computing to address scalability and latency issues. These advancements pave the way for more robust and proactive cybersecurity defenses in an increasingly interconnected digital landscape.]]>
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