Deep-RBF Networks for Anomaly Detection in Automotive Cyber-Physical Systems
This addresses the need for efficient anomaly detection in automotive CPS to enhance safety and reduce resource overhead, though it is incremental as it extends existing deep-RBF methods to regression tasks.
The paper tackles the problem of anomaly detection in automotive cyber-physical systems by proposing deep-RBF networks that perform both controller predictions and anomaly detection in a single network, reducing resource burden and latency. Results show these networks robustly detect adversarial attacks like physical and data poison attacks on hardware testbeds and real-world datasets without additional resource requirements.
Deep Neural Networks (DNNs) are popularly used for implementing autonomy related tasks in automotive Cyber-Physical Systems (CPSs). However, these networks have been shown to make erroneous predictions to anomalous inputs, which manifests either due to Out-of-Distribution (OOD) data or adversarial attacks. To detect these anomalies, a separate DNN called assurance monitor is often trained and used in parallel to the controller DNN, increasing the resource burden and latency. We hypothesize that a single network that can perform controller predictions and anomaly detection is necessary to reduce the resource requirements. Deep-Radial Basis Function (RBF) networks provide a rejection class alongside the class predictions, which can be utilized for detecting anomalies at runtime. However, the use of RBF activation functions limits the applicability of these networks to only classification tasks. In this paper, we show how the deep-RBF network can be used for detecting anomalies in CPS regression tasks such as continuous steering predictions. Further, we design deep-RBF networks using popular DNNs such as NVIDIA DAVE-II, and ResNet20, and then use the resulting rejection class for detecting adversarial attacks such as a physical attack and data poison attack. Finally, we evaluate these attacks and the trained deep-RBF networks using a hardware CPS testbed called DeepNNCar and a real-world German Traffic Sign Benchmark (GTSB) dataset. Our results show that the deep-RBF networks can robustly detect these attacks in a short time without additional resource requirements.