A Physics-Based Context-Aware Approach for Anomaly Detection in Teleoperated Driving Operations Under False Data Injection Attacks
Teleoperated driving (ToD) systems are a special type of cyber-physical system (CPS) where the operator remotely controls the steering, acceleration, and braking actions of the vehicle. Malicious actors may inject false data into communication channels to manipulate the teleoperator's driving commands to cause harm. Hence, protection of this communication is necessary for a safe operation of the target vehicle. However, according to the National Institute of Standards and Technology (NIST) cybersecurity framework, protection is not enough, and detecting an attack is necessary. Moreover, UN R155 mandates that vehicle fleets detect and log security incidents. Thus, the cyber-physical threats of ToD are modeled using the attack-centric approach in this paper. Then, an attack model with false data injection (FDI) on the steering control command is created from real vehicle data. A risk of this attack model is assessed for a last-mile delivery (LMD) application. Finally, a physics-based context-aware anomaly detection system (PCADS) is proposed to detect such false injection attacks, and preliminary experimental results are presented to validate the model.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionSteering ControlSimilar Papers 제목 키워드 기반
Enhancing anomaly detection with topology-aware autoencoders
Anomaly detection in high-energy physics is essential for identifying new physics beyond the Standard Model. Autoencoders provide a signal-agnostic approach but are limited by the topology of their latent space. This wor…
Anomaly DetectionAnomaly Awareness
We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this me…
Anomaly DetectionBIG-bench Machine LearningPhysics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection
3D point cloud anomaly detection plays a vital role in industrial manufacturing, yet it faces significant challenges due to the scarcity and high acquisition cost of real anomalous samples. The inherently anomaly-free tr…
3D Anomaly DetectionPoint CloudsContext-Aware Autoencoders for Anomaly Detection in Maritime Surveillance
The detection of anomalies is crucial to ensuring the safety and security of maritime vessel traffic surveillance. Although autoencoders are popular for anomaly detection, their effectiveness in identifying collective an…
Anomaly DetectionMachine Learning for Anomaly Detection in Particle Physics
The detection of out-of-distribution data points is a common task in particle physics. It is used for monitoring complex particle detectors or for identifying rare and unexpected events that may be indicative of new phen…
Anomaly Detection