Proactive Network Management Through AI-Powered Cybersecurity Solutions

Active cybersecurity requires smart systems that can predict, and counter attack before it damages network integrity. This study presents an AI-based model of proactive network management comprising a deep learning-based analysis of traffic, a graph-based anomaly detector, a predictive-based analytics framework, reinforcement learning to make an adapted decision, and an automated policy enforcer. The validation of the system was downloaded to the CICIDS2017 dataset under real-world conditions. The results of the experiments prove high performance in relation to the current models of intrusion detection reaching accuracy of 96.8 and precision and recall of 93.9 and 94.3, respectively, combined with F1 -score, 88.1 and mAP of 89.5. These results underscore the capability of the framework to provide predictive, adaptive and automated defense effectively cutting the false negative and increasing resiliency to zero-day and polymorphic threats. The results prove that active security with AI can become a transition to network management opportunities that do not depend on the timely response to threats but on the prospects of attack anticipation in the context of modern structures.