Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
Real-time systems are susceptible to adversarial factors such as faults and attacks, leading to severe consequences. This paper presents an optimal checkpoint scheme to bolster fault resilience in real-time systems, addressing both logical consistency and timing correctness. First, we partition message-passing processes into a directed acyclic graph (DAG) based on their dependencies, ensuring checkpoint logical consistency. Then, we identify the DAG’s critical path, representing the longest sequential path, and analyze the optimal checkpoint strategy along this path to minimize overall execution time, including checkpointing overhead. Upon fault detection, the system rolls back to the nearest valid checkpoints for recovery. Our algorithm derives the optimal checkpoint count and intervals, and we evaluate its performance through extensive simulations and a case study. Results show a 99.97% and 67.86% reduction in execution time compared to checkpoint-free systems in simulations and the case study, respectively. Moreover, our proposed strategy outperforms prior work and baseline methods, increasing deadline achievement rates by 31.41% and 2.92% for small-scale tasks and 78.53% and 4.15% for large-scale tasks.more » « less
-
Cyber-physical systems (CPSs) leverage computations to operate physical objects in real-world environments, and increasingly more CPS-based applications have been designed for life-critical applications. Therefore, any vulnerability in such a system can lead to severe consequences if exploited by adversaries. In this paper, we present a data predictive recovery system to safeguard the CPS from sensor attacks, assuming that we can identify compromised sensors from data. Our recovery system guarantees that the CPS will never encounter unsafe states and will smoothly recover to a target set within a conservative deadline. It also guarantees that the CPS will remain within the target set for a specified period. Major highlights of our paper include (i) the recovery procedure works on nonlinear systems, (ii) the method leverages uncorrupted sensors to relieve uncertainty accumulation, and (iii) an extensive set of experiments on various nonlinear benchmarks that demonstrate our framework's performance and efficiency.more » « less
-
Cyber-physical systems (CPS) are susceptible to physical attacks, and researchers are exploring ways to detect them. One method involves monitoring the system for a set duration, known as the time-window, and identifying residual errors that exceed a predetermined threshold. However, this approach means that any sensor attack alert can only be triggered after the time-window has elapsed. The length of the time-window affects the detection delay and the likelihood of false alarms, with a shorter time-window leading to quicker detection but a higher false positive rate, and a longer time-window resulting in slower detection but a lower false positive rate. While researchers aim to choose a fixed time-window that balances a low false positive rate and short detection delay, this goal is difficult to attain due to a trade-off between the two. An alternative solution proposed in this paper is to have a variable time-window that can adapt based on the current state of the CPS. For instance, if the CPS is heading towards an unsafe state, it is more crucial to reduce the detection delay (by decreasing the time-window) rather than reducing the false alarm rate, and vice versa. The paper presents a sensor attack detection framework that dynamically adjusts the time-window, enabling attack alerts to be triggered before the system enters dangerous regions, ensuring timely detection. This framework consists of three components: attack detector, state predictor, and window adaptor. We have evaluated our work using real-world data, and the results demonstrate that our solution improves the usability and timeliness of time-window-based attack detectors.more » « less