Improving Intrusion Detection Systems using Reinforcement Learning: Responding to New Cyber Attacks and Threats

Cyber threats are evolving quickly, making traditional intrusion detection systems (IDS) less efficient at detecting and responding to new attack vectors. Cyber attackers constantly change their tactics, making rule-based systems unable to identify new threats. Intelligent solutions are needed. This study proposes using Reinforcement Learning (RL) to improve intrusion detection systems (IDS) to detect existing attacks and dynamically adapt to new cyber threats. Our method trains RL models to identify, classify, and respond to possible intrusions in real time, learning from previous attack data and simulated scenarios. The suggested system uses RL to learn from an environment with diverse security risks, allowing the IDS to continuously adapt its threat detection tactics without manual rule generation or regular updates. RL's capacity to evaluate and respond to new attack patterns makes it more flexible and sensitive to zero-day attacks and complex persistent threats, which traditional systems miss. The research describes the RL-based IDS architecture, including reward functions to improve detection accuracy and response efficiency. A case study using the model in real-world network environments shows improved detection rates and fewer false positives than standard IDS approaches. The study also examines Q-learning and Deep Q Networks (DQN) RL algorithms to determine the best way to train the system on labelled and unlabelled network traffic. To prove the system works, detection accuracy, false positive rates, and real-time response times are examined. The results imply that RL-based intrusion detection systems can improve network security, giving a potential alternative for organisations that must adapt to new and complex cyber threats. As cyber threats evolve, this approach can be used to build proactive, adaptive IDS that can handle new attack patterns.