A Spatio-Temporal Approach to Mitigate Automotive Radar Spoofing Attacks

Cyber-physical system (CPS) has become an integral part of human life, ranging from aircraft to health care systems. The security of these critical components ensures its wider acceptability. Traditionally, many works to secure cyber-physical system (CPS) has been done in the cyber domain, like securing inter/intra CPS communication, securing the exposed software, rebuilding control input derived from sensor data post-digitization, using sensor fusion. All of this security software suffers from a basic attack wherein an attacker compromises the physical/analog sensing system. Researchers have made some progress in mitigating such attacks on physical/analog signals of CPS, the current state of the art methodology proposed in PyCRA uses temporal random signals for physical challenge-response authentication. Though this approach immensely enhances the capability of identifying the sensor attacks, it fails to provide any recovery mechanism to the system. Recent work like Dutta et al., 2017 tries to address this by introducing recursive least squares (RLS) based recovery mechanisms over PyCRA. Although these systems provide some recovery in trivial scenarios, they fail during longer attacks and also result in loss of control because of longer/frequent random no-signal periods. Which could be catastrophic in real-time systems. This work presents Spatio-Temporal Challenge-Response (STCR), an authentication scheme designed to protect active sensing systems against physical attacks occurring in the analog domain. This system utilizes multiple beam-forming and provides physical challenge-response authentication (CRA) in both spatial and temporal domain. Thus providing a much more resilient authentication mechanism that not only detects malicious attacks, but also provides recovery from them. We demonstrate the resilience and effectiveness of STCR over the state of the art in detecting and mitigating attacks through several experiments using a car following (CF) model. This model deploys CPS in the follower car to sense the lead car’s relative position and maintain a safe distance by manipulating acceleration

Fault-Tolerant Control of Autonomous Ground Vehicle under Actuator and Sensor

Unmanned ground vehicles have a wide range of potential applications including autonomous driving, military surveillance, emergency responses, and agricultural robotics, etc. Since such autonomous vehicles need to operate reliably at all times, despite the possible occurrence of faulty behaviors in some system components, the development of fault-tolerant control schemes is a crucial step in ensuring reliable and safe operations. In this research, a fault-tolerant control scheme is developed for a nonlinear ground vehicle model with possible occurrence of both actuator faults in the form of loss of effectiveness (LOE) and sensor bias faults. Based on the vehicle and fault models under consideration, the unknown fault parameters are estimated online using adaptive estimation methods. The estimated fault parameters are used for accommodating the fault effect to maintain satisfactory control performance even in the presence of faults. Real-time algorithm implementation and demonstration using the Qbot2e ground robot by Quanser are conducted to show the effectiveness of the fault-tolerant control algorithm

Exploring Granger causality in dynamical systems modeling and performance monitoring

Data-driven approaches are becoming increasingly crucial for modeling and performance monitoring of complex dynamical systems. Such necessity stems from complex interactions among sub-systems and high dimensionality that render majority of rst-principle based methods insucient. This work explores the capability of a recently proposed probabilistic graphical modeling technique called spatiotemporal pattern network (STPN) in capturing Granger causality among observations in a dynamical system. In this context, we introduce the notion of Granger-STPN (G-STPN) inspired by the notion of Granger causality. We compare the metrics used in the two frameworks for increasing memory in a dynamical system, and show that the metric for G-STPN can be approximated by transfer entropy. We apply this new framework for anomaly detection and root cause analysis in a robotic platform