Implementation of AI-Driven Intrusion Detection Systems to Analyze Anomalies and Network Traffic Patterns in Healthcare Networks

AI-powered intrusion detection systems (IDS) for healthcare networks are discussed in this paper. We aim to enhance their safety and safeguard patient data from potential dangers. Gathering and categorizing network traffic data using entropy estimates and information gain assessments helps detect abnormalities. Decision trees and ensemble learning provide a robust framework that captures complicated data patterns without overfitting. The proposed AI-driven IDS outperforms Signature-Based and Anomaly-Based IDSs in threat detection, speed, growth, and resource utilization. The method has 98.5% accuracy and 1.5% and 2.0% false positives and negatives. This indicates its outstanding real-time processing and adaptability to new threats. Its strong trustworthiness and privacy preservation ratings demonstrate that the technology can protect healthcare data. The findings demonstrate that healthcare facilities must employ current IDS systems. This will boost security, patient outcomes, and digital healthcare system confidence. Our AI-powered method enables scalable protection of critical patient data in a complex digital environment as cyber risks mount.