Detecting Attacks Against Robotic Vehicles - A Control Invariant Approach

Explore a comprehensive approach to detecting cyber and physical attacks on robotic vehicles in this 25-minute conference talk. Delve into the Control Invariant (CI) method, which aims to mitigate the risks of physical malfunction and mission disruption. Learn about attack models, control invariant extraction, control loop reverse engineering, and memory location identification. Discover the process of monitoring parameter selection and evaluate the effectiveness and efficiency of this approach through simulated attacks and case studies. Gain valuable insights into enhancing the security of robotic vehicles against potential threats.

Intro
Robotic Vehicles (RVS)
Cyber vs. Physical Attacks
Attack Model
Our Control Invariant (CI) Approach
Control Invariant Extraction
Control Loop Reverse Engineering
Identifying Memory Locations
Monitoring Parameter Selection
Evaluation: Subject Vehicles
Simulated Attacks
Evaluation: Effectiveness
Evaluation: Efficiency
Case Study
Conclusion