Rômulo Meira-Góes

Cyber Physical Systems (CPS) integrate computational intelligence with physical processes, improving efficiency of complex infrastructures. CPS subsystems are prone to faults arising from malfunctions, degradation, and wear and tear. Such faults pose a risk to maintaining system-level performance, particularly by interrupting the subsystem dependencies. This paper presents an online model-based fault-tolerant control framework for CPSs modeled as Finite State Automata (FSA). We introduce fault-tolerant strategies that use quantified FSA-based risk measures. These strategies enable proactive adjustment of the control policy such that solutions that may lead to control infeasibility under subsystem faults are prevented. Unlike approaches reliant on historical fault data, the proposed methods are suitable for systems with no prior fault records or with dynamically evolving requirements. The efficacy of these approaches is demonstrated through two case studies, highlighting their ability to maintain control feasibility in the presence of faults. Additionally, a comprehensive sensitivity analysis on randomly generated FSAs compares the fault-tolerance of the proposed controllers against cost-optimal control design. Results show that FSA-based risk quantification improves fault tolerance in online control.

Critical real-world applications strongly rely on Cyber-physical systems (CPS), but their dependence on communication networks introduces significant security risks, as attackers can exploit vulnerabilities to compromise their integrity and availability. This work explores the topic of cybersecurity in the context of CPS modeled as discrete event systems (DES), focusing on recovery strategies following the detection of cyberattacks. Specifically, we address actuator enablement attacks and propose a method that preserves the system's full valid behavior under normal conditions. Upon detecting an attack, our proposed solution aims to guide the system toward a restricted yet robust behavior, ensuring operational continuity and resilience. Additionally, we introduce a property termed AE-robust recoverability, which characterizes the necessary and sufficient conditions for recovering a system from attacks while preventing further vulnerabilities. Finally, we showcase the proposed solution through a case study based on a manufacturing system.

Sensor attacks compromise the reliability of cyber-physical systems (CPSs) by altering sensor outputs with the objective of leading the system to unsafe system states. This paper studies a probabilistic intrusion detection framework based on $λ$-sensor-attack detectability ($λ$-sa), a formal measure that evaluates the likelihood of a system being under attack based on observed behaviors. Our framework enhances detection by extending its capabilities to identify multiple sensor attack strategies using probabilistic information, which enables the detection of sensor attacks that were undetected by current detection methodologies. We develop a polynomial-time algorithm that verifies $λ$-sa detectability by constructing a weighted verifier automaton and solving the shortest path problem. Additionally, we propose a method to determine the maximum detection confidence level ($λ$*) achievable by the system, ensuring the highest probability of identifying attack-induced behaviors.

Cyber-physical systems (CPS) with reinforcement learning (RL)-based controllers are increasingly being deployed in complex physical environments such as autonomous vehicles, the Internet-of-Things (IoT), and smart cities. An important property of a CPS is tolerance; i.e., its ability to function safely under possible disturbances and uncertainties in the actual operation. In this paper, we introduce a new, expressive notion of tolerance that describes how well a controller is capable of satisfying a desired system requirement, specified using Signal Temporal Logic (STL), under possible deviations in the system. Based on this definition, we propose a novel analysis problem, called the tolerance falsification problem, which involves finding small deviations that result in a violation of the given requirement. We present a novel, two-layer simulation-based analysis framework and a novel search heuristic for finding small tolerance violations. To evaluate our approach, we construct a set of benchmark problems where system parameters can be configured to represent different types of uncertainties and disturbances in the system. Our evaluation shows that our falsification approach and heuristic can effectively find small tolerance violations.

The adoption of cyber-physical systems (CPS) is on the rise in complex physical environments, encompassing domains such as autonomous vehicles, the Internet of Things (IoT), and smart cities. A critical attribute of CPS is robustness, denoting its capacity to operate safely despite potential disruptions and uncertainties in the operating environment. This paper proposes a novel specification-based robustness, which characterizes the effectiveness of a controller in meeting a specified system requirement, articulated through Signal Temporal Logic (STL) while accounting for possible deviations in the system. This paper also proposes the robustness falsification problem based on the definition, which involves identifying minor deviations capable of violating the specified requirement. We present an innovative two-layer simulation-based analysis framework designed to identify subtle robustness violations. To assess our methodology, we devise a series of benchmark problems wherein system parameters can be adjusted to emulate various forms of uncertainties and disturbances. Initial evaluations indicate that our falsification approach proficiently identifies robustness violations, providing valuable insights for comparing robustness between conventional and reinforcement learning (RL)-based controllers

Trajectory planning is a critical process that enables autonomous systems to safely navigate complex environments. Signal temporal logic (STL) specifications are an effective way to encode complex temporally extended objectives for trajectory planning in cyber-physical systems (CPS). However, planning from these specifications using existing techniques scale exponentially with the number of nested operators and the horizon of specification. Additionally, performance is exacerbated at runtime due to limited computational budgets and compounding modeling errors. Decomposing a complex specification into smaller subtasks and incrementally planning for them can remedy these issues. In this work, we present a way to decompose STL requirements temporally to improve planning efficiency and performance. The key insight in our work is to encode all specifications as a set of reachability and invariance constraints and scheduling these constraints sequentially at runtime. Our proposed technique outperforms the state-of-the-art trajectory synthesis techniques for both linear and non linear dynamical systems.

A safety verification task involves verifying a system against a desired safety property under certain assumptions about the environment. However, these environmental assumptions may occasionally be violated due to modeling errors or faults. Ideally, the system guarantees its critical properties even under some of these violations, i.e., the system is robust against environmental deviations. This paper proposes a notion of robustness as an explicit, first-class property of a transition system that captures how robust it is against possible deviations in the environment. We modeled deviations as a set of transitions that may be added to the original environment. Our robustness notion then describes the safety envelope of this system, i.e., it captures all sets of extra environment transitions for which the system still guarantees a desired property. We show that being able to explicitly reason about robustness enables new types of system analysis and design tasks beyond the common verification problem stated above. We demonstrate the application of our framework on case studies involving a radiation therapy interface, an electronic voting machine, a fare collection protocol, and a medical pump device.