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 a...
| Authors | Alexandre Amaral, Fernando Moro, Ana Malheiro |
|---|---|
| Primary category | cs.CR |
| Published | 22 August 2026 |
| Last updated at source | 26 August 2026 |
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