Secure and Trustworthy Operation of a Differential Drive UGV Using Random Forest Under Power Constraints

Command injection attacks represent a significant threat to autonomous unmanned ground vehicles (UGVs), enabling adversaries to manipulate vehicle behavior while evading traditional detection mechanisms. This paper introduces a novel physics-aware approach to detecting such attacks in differential-drive systems by leveraging the inherent electromechanical constraints that malicious commands typically violate. This research develops a framework that systematically integrates conventional command monitoring with physics-aware features derived from motor power relationships, including power balance, asymmetry, and current-velocity correlations. Experimental evaluation on a physical UGV demonstrates that the physics-enhanced detection achieves a 93% F1-score, representing a 45% reduction in missed attacks compared to conventional approaches. Feature importance analysis confirms that physics-aware features contribute 46% of the model’s predictive power, with UGV power asymmetry and motor balance providing particularly strong signals for attack detection. The implementation requires minimal computational resources (3.57ms processing time, 194KB memory footprint), making it suitable for real-time deployment on resource-constrained platforms. By creating a security layer that reveals anomalous behavior through tracking UGV physical characteristics, this research advances the development of resilient autonomous systems that maintain operational integrity even under sophisticated attack scenarios. Therefore, this approach ultimately enhances the trustworthiness of automation in critical applications.