Paper Title
AI-POWERED NETWORK INTRUSION DETECTION SYSTEM

Abstract
Amid a terrain of precipitously advanced cyber pitfalls, conventional security measures regularly need enough effectiveness to stem the attack. AI-Powered Network Intrusion Discovery Frameworks (NIDS) are taking advertising by storm as an ultramodern mechanical approach that applies artificial intelligence invention to ameliorate cybersecurity. This composition focuses on the AI-driven NIDS’s essential studies, which are more advanced than their classical shapes, their preferences over the traditional bones, and the issues related to their operation. Our examination of how machine literacy algorithms can fete irregularities and implicit troubles in real-time will accentuate the transformative impacts of AI on network security. Other than that, we uncovered the part of nonstop literacy and adaptation in these fabrics so that they can be continuously successful against advancing troubles. These are the challenges that associations are defying as they attempt to cover their touchy information and keep their functional systems complete. In discrepancy, AI- driven Organize Interruption Detection Systems can do that. This composition expects to mediate the anthology’s understanding of the mechanics of these further sophisticated fabrics and their corridor in softening defense systems and securing advanced coffers in the precipitously more delicate cyber scene. Keywords - AI, Network Intrusion Detection System, cyber-security, machine learning, real-time threat detection, anomaly detection.