Real-Time Adaptive Intrusion Detection in ROS-Based Mobile Robots through Enhanced Kalman Filtering and Federated Learning

**Abstract:** This paper introduces a novel methodology for real-time adaptive intrusion detection systems (IDS) integrated with ROS-based mobile robots. Traditional ROS security measures often lack dynamic adaptivity against evolving cyber threats. This research proposes an enhanced Kalman Filtering (EKF) based anomaly detection system, coupled with a federated learning approach to distribute training and mitigate data dependence. The integrated system demonstrates robustness against previously unseen attacks, providing a significant improvement in security posture, and enabling safe and reliable operation in unpredictable environments.