Application of deep reinforcement learning algorithms for automatic threat detection and response in dynamic network environments to improve cybersecurity

In the present environment, as cybersecurity attacks become more sophisticated and frequent, classic protection techniques, such as rule-based firewalls and signature-based detection systems, are no longer effective. Modern cyberattacks require innovative solutions that can change and react in real time. Deep reinforcement learning (DRL) is a subfield of artificial intelligence that efficiently solves complicated decision-making issues in numerous domains, such as cybersecurity. This research aims to improve cybersecurity by establishing deep reinforcement learning algorithms for automated threat detection and response in dynamic network environments. To address these problems, this study proposed a novel Improved Mayfly Optimized Deep Deterministic Policy Gradient (IMO-DDPG) to automate threat detection and improve cybersecurity response in dynamic network environments. Collect real-time network intrusion datasets accessible to the public to train the DRL model. The data was preprocessed using normalization, for feature extraction Principal Component Analysis (PCA). To improve performance in an increasingly developing environment, the study customizes DRL algorithms with an emphasis on continuous action spaces, adversarial training, and customized reward structures. The results indicate that the IMO-DDPG method outperforms accuracy (94.5%), recall (94.0%), precision (92.5%), and F1-score (93.5%) to compare existing algorithms. The remarkable success rate, highest average reward, and superior efficiency in interpretation significantly improve threat detection and response capabilities. The results show the suggested algorithms for automated detection and response, highlighting the right decision, can significantly enhance cybersecurity defences.