Intelligent Real-Time Anomaly Detection and Autoblocking Framework for Server Security using Machine Learning

Cybersecurity threats continue to escalate, leaving servers increasingly vulnerable to compromise and disruption. Once breached, recovery often demands direct intervention from IT or security teams, highlighting the limitations of traditional rule-based and signature-driven defense systems. These conventional approaches struggle to detect novel or evolving attacks. To address this gap, we propose an intelligent, automated, real-time anomaly detection and autoblocking framework powered by machine learning. The system integrates Random Forest and Isolation Forest algorithms to analyze live network traffic, supported by Scapy for packet capture and manipulation. By continuously monitoring traffic, the framework identifies abnormal patterns and transitions seamlessly from detection to mitigation - blocking suspicious IPs, terminating compromised connections, and preventing further exploitation. Designed with scalability and low latency in mind, the framework adapts to dynamic environments, offering a resilient and autonomous solution for modern server security.