Adaptive cyber defence learning reinforcement

Cybersecurity has become an increasingly critical concern due to the rising complexity and frequency of cyberattacks. Traditional static defence mechanisms, which rely on predefined rules or known threat signatures, are often insufficient in countering these evolving threats. Static defences fail to adapt to the dynamic nature of modern cyber-attacks, particularly zero- day vulnerabilities and advanced persistent threats (APTs). This paper explores the application of Reinforcement Learning (RL) in the development of adaptive cyber defence systems that can dynamically respond to evolving threats in real-time. By employing RL, a defender agent is trained to learn optimal strategies through continuous interactions with a simulated environment that replicates diverse attack vectors and network scenarios. The RL-based system not only detects and mitigates known threats but also demonstrates remarkable adaptability to unknown attack patterns. Experimental results reveal that RL-based defence systems achieve superior detection accuracy, faster response times, and lower false positive rates compared to traditional methods. The findings underscore the transformative potential of RL in modern cybersecurity frameworks, paving the way for robust, automated, and scalable defence solutions that can keep pace with the rapidly changing threat landscape.