Autonomous Cyber Defense: Real-Time Attack Detection and Mitigation in Software-Defined Networks Using Machine Learning
Autonomous response has evolved into a timing-critical challenge rather than solely a matter of detection accuracy. In recent intrusions, the interval between initial access and the first lateral movement has been observed to be as short as 27 seconds, a window that precludes any human-in-the-loop workflow. This paper presents a closed-loop framework that detects and blocks attacks in software-defined networks without operator involvement, evaluating its performance against this stringent temporal constraint rather than relying exclusively on detection accuracy. An automated data pipeline collects IP flows and aggregates them into labeled training data, while a prevention module selects and trains candidate classifiers and issues blocking rules directly to the SDN controller. In a SYN flooding denial of service case study, the deployed K-Nearest Neighbors classifier achieved an F1 score of 96.7% and the cycle from flow availability to enforced block completed in 21 seconds, below the fastest breakout time reported to date.