Explainable AI-Powered Networking Intrusion Detection Systems

Networking Intrusion Detection Systems (NIDS) are essential for identifying and mitigating cyberthreats in modern Networking infrastructures. Modern NIDS still struggle with scalability, data imbalance, and high false positive rates despite significant advancements. This paper proposes a system that integrates state-of-the-art machine learning (ML) methods with Explainable AI (XAI) approaches to improve the efficacy, accuracy, and interpretability of NIDS. The study evaluates existing systems, identifies their flaws, and proposes an enhanced architecture that includes real-time threat adaptation, scalable processing, and an intuitive dashboard for security analysts. The experiment's results demonstrate a significant drop in false positives, an increase in detection rates, and improved user insights into system selections.