AI-based network intrusion detection focuses on identifying suspicious activities targeting network-accessible services by analyzing traffic patterns, request characteristics, and connection-related behavior. It involves examining HTTP requests, monitoring remote-access connection activity, extracting relevant traffic and event features, and identifying deviations or sequences that may indicate attempted exploitation, reconnaissance, or unauthorized access. By combining machine learning-based anomaly detection with transformer-based sequence analysis, this approach helps distinguish potentially malicious activity from legitimate service interactions and identify intrusion indicators across web applications and network services. The resulting detections support timely alert generation, security investigation, and the implementation of appropriate defensive controls to strengthen network and service security.