Using Reinforcement Learning to Weaken DDoS Attacks in Real-Time

Using Reinforcement Learning to Weaken DDoS Attacks in Real-Time Abstract: Distributed Denial of Service (DDoS) attacks are an escalating cybersecurity threat, disrupting online services by overwhelming network resources. Traditional mitigation techniques, such as firewalls and rate limiting, often struggle to adapt to evolving attack patterns. This research explores the application of Reinforcement Learning (RL) as an adaptive and intelligent defense mechanism against DDoS attacks. Unlike traditional machine learning models, RL continuously learns and improves its strategies through interaction with the network environment, making it particularly effective for dynamic attack scenarios. The study evaluates RL-based mitigation techniques, comparing their effectiveness against conventional methods and highlighting their potential to revolutionize real-time network security. Keywords: DDoS Mitigation, Reinforcement Learning, Cybersecurity, Machine Learning, Network Security