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This paper tackles the problem of making complex resource-constrained cyber-physical systems (CPS) resilient to sensor anomalies. In particular, we present a framework for checkpointing and roll-forward recovery of state-estimates in nonlinear, hierarchical CPS with anomalous sensor data. We introduce three checkpointing paradigms for ensuring different levels of checkpointing consistency across the hierarchy. Our framework has algorithms implementing the consistent paradigm to perform accurate recovery in a time-efficient manner while managing the tradeoff with system resources and handling the interplay between diverse anomaly detection systems across the hierarchy. Further in this work, we detail bounds on the recovered state-estimate error, maximum tolerable anomaly duration and the accuracy-resource gap that results from the aforementioned tradeoff. We explore use-cases for our framework and evaluate it on a case study of a simulated ground robot to show that it scales to multiple hierarchies and performs better than an extended Kalman filter (EKF) that does not incorporate a checkpointing procedure during sensor anomalies. We conclude the work with a discussion on extending the proposed framework to distributed systems.

Closed-loop control systems employ continuous sensing and actuation to maintain controlled variables within preset bounds and achieve the desired system output. Intentional disturbances in the system, such as in the case of cyberattacks, can compromise reachability of control goals, and in several cases jeopardize safety. The increasing connectivity and exposure of networked control to external networks has enabled attackers to compromise these systems by exploiting security vulnerabilities. Attacks against safety-critical control loops can not only drive the system over a trajectory different from the desired, but also cause fatal consequences to humans. In this paper we present a physics-based Intrusion Detection System (IDS) aimed at increasing the security in control systems. In addition to conventional process state estimation for intrusion detection, since the controller cannot be trusted, we introduce a controller state estimator. Additionally, we make our detector context-aware by utilizing sensor measurements from other control loops, which allows to distinguish and characterize disturbances from attacks. We introduce adaptive thresholding and adaptive filtering as means to achieve context-awareness. Together, these methodologies allow detection and localization of attacks in closed-loop controls. Finally, we demonstrate feasibility of the approach by mounting a series of attacks against a networked Direct Current (DC) motor closed-loop speed control deployed on an ECU testbed, as well as on a simulated automated lane keeping system. Among other application domains, this set of approaches is key to support security in automotive systems, and ultimately increase road and passenger safety., Comment: Embedded Security in Cars (ESCAR) USA 2021

Automotive systems continuously increase their dependency on Electronic Control Units (ECUs) and become more interconnected to improve safety, comfort and Advanced Driving Assistance Systems (ADAS) functions to passengers and drivers. As a consequence of that trend, there is an expanding attack surface which may potentially expose vehicle's critical functions to cyberattacks. It is possible for an adversary to reach the underlying Control Area Network (CAN) through a compromised node or external-facing network interface, and launch masquerading attacks that can compromise road and passenger safety. Due to lack of native authentication in the CAN protocol, an approach to detect masquerading attacks is to use ECU voltage fingerprinting schemes to verify that the messages are sent by authentic ECUs. Though effective against simple masquerading attacks, prior work is unable to detect attackers such as hardware Trojans, which can mimic ECU voltages in addition to spoofing messages. We introduce a novel Two-point ECU Fingerprinting scheme and demonstrate efficacy in a controlled lab setting and on a moving vehicle. Our results show that our proposed two-point fingerprinting scheme is capable of an overall F1-score over 99.4%. The proposed approach raises the bar for attackers trying to compromise automotive security both remotely and physically, therefore improving security and safety of autonomous vehicles.