K. A. Al Shamaileh

A Machine Learning-Powered Solution for Safe Autonomous Robotic Ground Navigation in Cyber-Contested Environments

Results suggest that the proposed solution allows the detection of authentic and attacked messages with more than 98% average accuracy and sub-millisecond prediction time, which is ideal for real-time classification, especially in fixed-route applications, e.g., public transportation.