Distributed Denial of Service Attack Mitigation Using Reinforcement Learning

A Distributed Denial of Service (DDoS) attack poses a significant threat by inundating a target with a deluge of traffic, rendering its services inaccessible. In response to this menace, reinforcement learning (RL) emerges as a promising avenue for effectively detecting and mitigating DDoS attacks within a simulated environment. Central to this approach is the utilization of a controller, which dynamically manages network resources while integrating feedback from the environment. Through RL, the system develops an adaptive detection policy, leveraging insights gleaned from analyzing pertinent traffic features in packet flow data. RL agent is trained to discern between benign and malicious traffic, with the overarching goal of maximizing cumulative rewards over time. Upon detection of a DDoS attack, the controller orchestrates a suite of mitigation strategies, thereby safeguarding the integrity and availability of the network. This paper delves into the intricate workings of this RL-based approach, elucidating its efficacy in fortifying network defenses against the pernicious threat of DDoS attacks.