Intrusion Response System Man-In-The-Middle Cyber Attack with Random Forest Method

This poster presents the development and evaluation of an Intrusion Response System (IRS) designed to detect and automatically respond to Man-in-the-Middle (MITM) cyber attacks in Cyber-Physical System (CPS) environments using a Random Forest classification model. The proposed system captures network traffic, extracts flow-based features through CICFlowMeter, performs preprocessing, and applies a trained Random Forest classifier to distinguish normal and malicious traffic in real time. Experimental results demonstrate strong classification performance with an AUC of 0.959, 87% accuracy, and effective automatic firewall-based IP blocking, while maintaining low latency and stable performance under intensive network conditions. Overall, the poster highlights a practical, resilient, and real-time security solution for mitigating MITM attacks in modern CPS networks.