Human-In-The-Loop Threat Detection

Abstract

The increasing complexity of cyber threats has made automated systems essential for detecting and preventing malicious activities. However, fully automated models often struggle to handle ambiguous or novel attacks that require contextual understanding. This project, Human-in-the-Loop Threat Detection and Prevention System, integrates machine learning–based detection with human expertise to create a hybrid security framework. The system continuously monitors network logs to identify anomalies such as brute-force attacks, phishing attempts, and port scans. Detected threats are automatically categorized—known attacks are handled through automated prevention mechanisms, while uncertain cases are forwarded to a web-based human interface for expert review and decision-making. Human feedback is recorded and used to retrain the model, improving its accuracy and adaptability over time. This collaborative approach bridges the gap between automation and human judgment, enabling faster, more accurate, and adaptive cyber defense against evolving threats.