MCP-Based Context-Aware System Monitoring and Threat Detection Agent

Abstract: This paper presents an MCP-Based Context-Aware System Monitoring and Threat Detection Agent, an intelligent, real-time cybersecurity monitoring platform leveraging the Model Context Protocol (MCP) to deliver context-aware threat detection and automated response capabilities. Traditional Security Information and Event Management (SIEM) systems rely on static rule-based engines that produce false-positive rates of 25–45%, suffer from alert fatigue, and fail to detect multi-stage Advanced Persistent Threats (APTs). The proposed system integrates a FastAPI backend, PostgreSQL 16 storage, WireGuard VPN encryption, and a Bootstrap 5 web dashboard to provide unified, real-time visibility across network traffic, system logs, and behavioral metrics. The MCP AI agent maintains a rolling context window over incoming security events, enabling temporal correlation, multi-stage attack detection, lateral movement identification, and significant reduction of false positives through composite threat scoring. Validation results demonstrate a 67% reduction in false positives, sub-3-second automated mitigation response, throughput exceeding 6,200 concurrent events per second, an AUC of 0.94 on the ROC curve, and 60–75% reduction in operational costs versus commercial SIEM solutions. All three functional modules—Data Collection & Traffic Monitoring, Threat Analysis & Context Awareness, and Alerting & Secure Notification—have been implemented and validated in a prototype environment over a 30-day test period.

Keywords: Model Context Protocol (MCP), Cybersecurity, Real-Time Threat Detection, Context-Aware AI, SIEM, FastAPI, WireGuard VPN, Anomaly Detection, Automated Incident Response.