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Hazard analysis to identify risks due to cyber attacks is crucial to ensure cyber-physical systems' safety and correct their behavior. This study proposes a framework to analyze cyber-physical systems (CPS) risks using system dynamics to identify the critical physical components and hazardous interactions. We divide the input space into three regions, namely safe, alarm, and hazard zones, based on some given information and the dynamics of the CPS. These regions show how by manipulation of the critical inputs system can be driven to some predefined region. Moreover, using the concept of gap metric, we refine the list of the input value of vulnerable components to update the regions. Also, as a part of hazard analysis, we study and obtain the time needed to operate the system from a safe region to a hazard region by manipulating the inputs. The proposed framework uses the physical system's model dynamics to simulate safe, alarm, and hazard mode activity. This framework's primary goal is to provide decision-makers with a framework to evaluate CPS safety measures to implement adequate safety mechanisms. Model dynamics of continuous stirred tank reactor system (CSTR) has been employed to simulate the process under different manipulation of the input signal based on the proposed framework to identify the critical inputs and hazardous scenarios. 2022 This work was made possible by grant #QUHI-CENG-21/22-2 from Qatar University . The statements made herein are solely the responsibility of the authors. Scopus

The increased connectivity of cyber-physical systems (CPS) to enterprise networks raises challenging security concerns. Detecting attacks on CPS is a vital step to improving their security. Most of the existing attack detectors are for CPS with linear dynamics. In this study, an investigation is made for designing a detector of deception attacks in a cyber-physical linear parameter varying (LPV) system in the presence of packet loss. A model-based attack detector is designed to detect deception attacks on output measurements, actuators, or schedule variables. With an unreliable network, the LPV attack detector enhances detection capacity and attenuates disturbances on the detector module to improve detection accuracy. During communication, packet loss results in network unreliability, which is modeled by the Bernoulli process. Stochastic stability is used to determine LPV attack detector parameters. A residual signal is built by comparing the detector's output and the actual sensor measurement and provides the deception attack's essential data. However, the packet loss causes some impulse signals in the residual signal, resulting in false alarms. Therefore, the detector module is equipped with a median filter to suppress packet loss's effect on evaluating the residual signal. Tests and validations of the proposed approach are performed on a two-tank system and a continuous stirred tank reactor. According to evaluation results conducted on two testbeds, the proposed method accurately detects deception attacks even when there is packet loss. 2022 John Wiley & Sons Ltd. information Qatar University, Grant/Award Number: #QUHI-CENG-21/22-2 This work was made possible by the Grant #QUHI-CENG-21/22-2 from the Qatar University. The statements made herein are solely the responsibility of the authors. Scopus

Cyberattacks on Cyber-Physical Systems (CPS) are on the rise due to CPS increased networked connectivity and may cause costly environmental hazards as well as human and financial loss. This necessitates designing and implementing proper automatic cybersecurity defensive mechanisms to protect CPS from cyberattacks. This paper studies and surveys Intrusion Response Systems (IRS) by considering their taxonomy, countermeasures pool, general architecture, and the different decision-making approaches utilized in the literature to build resilient CPS that can maintain a safe and stable operational behaviour and minimize the effect of cyberattacks. Recent new advances using Reinforcement Learning (RL) algorithms for IRS designs are also surveyed and discussed. Additionally, the paper highlights open research questions and sketches directions for future work.

Background Coronavirus disease 2019 is an emerging respiratory disease caused by a novel coronavirus effect on 10-20% of total healthcare workers and was first detected in December 2019 in Wuhan, China. This study was designed to assess effect of COVID-19 stressors on healthcare workers’ performance and attitude. A descriptive cross sectional research design was used. A convenient sample (all available healthcare workers) physicians “112,”, nurses “183,” pharmacists “31,” and laboratory technicians “38” was participated to conduct aim of the study. Utilize the study with two tools; online self-administrated questionnaire to assess level of knowledge, attitude, and infection control measures regarding coronavirus disease 2019 and COVID-19 stress scales to assess the varied stressors among healthcare workers. Results More than three quarter of the studied participants had satisfactory level of knowledge and infection control measures. Approximately all of the studied participants had positive attitude regarding COVID-19. A total of 57.4% of the studied medical participants had moderate COVID-19 psychological stress levels, while 49.1% of the studied paramedical participants had moderate COVID-19 psychological stress levels. But less than one quarter had severe COVID-19 psychological stress levels. There is a significant correlation between COVID-19 psychological stressor levels and satisfactory level of knowledge among medical participants. Conclusion/implications for practice Most of healthcare workers had satisfactory level of knowledge, infection control measures, and positive attitude regarding COVID-19. Most of them had moderate COVID-19 psychological stress levels.

Safety risk assessment is an essential process to ensure a dependable Cyber-Physical System (CPS) design. Traditional risk assessment considers only physical failures. For modern CPS, failures caused by cyber attacks are on the rise. The focus of latest research effort is on safety-security lifecycle integration and the expansion of modeling formalisms for risk assessment to incorporate security failures. The interaction between safety and security lifecycles and its impact on the overall system design, as well as the reliability loss resulting from ignoring security failures are some of the overlooked research questions. This paper addresses these research questions by presenting a new safety design method named Cyber Layer Of Protection Analysis (CLOPA) that extends existing LOPA framework to include failures caused by cyber attacks. The proposed method provides a rigorous mathematical formulation that expresses quantitatively the trade-off between designing a highly-reliable versus a highly-secure CPS. We further propose a co-design lifecycle process that integrates the safety and security risk assessment processes. We evaluate the proposed CLOPA approach and the integrated lifecycle on a practical case study of a process reactor controlled by an industrial control testbed, and provide a comparison between the proposed CLOPA and current LOPA risk assessment practice., Preprint version of the published paper

Traditional techniques for Cyber-Physical Systems (CPS) security design either treat the cyber and physical systems independently, or do not address the specific vulnerabilities of real time embedded controllers and networks used to monitor and control physical processes. In this work, we develop and test an integrated model-based approach for CPS security risk assessment utilizing a CPS testbed with real-world industrial controllers and communication protocols. The testbed monitors and controls an exothermic Continuous Stirred Tank Reactor (CSTR) simulated in real-time. CSTR is a fundamental process unit in many industries, including Oil & Gas, Petrochemicals, Water treatment, and nuclear industry. In addition, the process is rich in terms of hazardous scenarios that could be triggered by cyber attacks due to the lack of possible mechanical protection. The paper presents an integrated approach to analyze and design the cyber security system for a given CPS where the physical threats are identified first to guide the risk assessment process. A mathematical model is derived for the physical system using a hybrid automaton to enumerate potential hazardous states of the system. The cyber system is then analyzed using network and data flow models to develop the attack scenarios that may lead to the identified hazards. Finally, the attack scenarios are performed on the testbed and observations are obtained on the possible ways to prevent and mitigate the attacks. The insights gained from the experiments result in several key findings, including the expressive power of hybrid automaton in security risk assessment, the hazard development time and its impact on cyber security design, and the tight coupling between the physical and the cyber systems for CPS that requires an integrated design approach to achieve cost-effective and secure designs. 2020 Elsevier Ltd This research was made possible by NPRP 9-005-1-002 grant from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

In this paper, we investigate the process of detection of False Data Injection (FDI) in a Linear Parameter Varying (LPV) cyber-physical system (CPS). We design a model based FDI detector capable of detecting false data injections on output measurements and scheduling variables. To improve the detection accuracy of FDI attacks, the attack detector design uses the performance metric H- to maximize the detection capability of the detector module to effectively detect FDI attacks. On the other hand, it uses the H? metric to minimize the effect of disturbance on the detector module given an unreliable network. We assume that the network unreliability comes from packet dropout that we modeled as Bernoulli process. The FDI attack detector is designed such that H- and H? performance metrics are maintained despite packet dropout. Based on stochastic stability, we define a set of sufficient Linear Matrix Inequalities (LMI) that we solve as a multi-objective optimization problem to obtain the detector gain. The obtained detector gain is used for estimating the current system state and current output measurement using the system input, manipulated measurements and manipulated scheduling variables. Then, the output of the detector is compared with the actual sensor measurement. The resulting residual signal carries the information about the FDI attack. The proposed approach is tested and validated on a two-tank system. The evaluation results demonstrate that the proposed detector is able to detect FDI attacks. - 2019 IEEE. This work was made possible by NPRP grant # NPRP9-005-1-002 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

The Continuous Stirred Tank Reactor (CSTR) is an essential process component in a variety of industries. Accurate modeling and simulation of the CSTR is crucial for efficient control and safety system design. In this paper, we present a novel hybrid dynamical model for the CSTR that supports accurate model-based design for CSTR safety and security systems. We first derive the system model then show how both the safety and security systems could be formally designed from the hybrid automaton. Finally, we discuss the generalization of the approach to other cyber physical systems.

Aircraft and spacecraft electrical power distribution systems are critical to overall system operation, but these systems may experience faults. Early fault detection makes it easier for system operators to respond and avoid catastrophic failures. This paper discusses a fault detection scheme based on a tunable generalized likelihood algorithm. We discuss the detector algorithm, and then demonstrate its performance on test data generated from a spacecraft power distribution testbed at NASA Ames. Our results show high detection accuracy and low false alarm rates.

Integrated drive generators (IDGs) are the main source of electrical power for a number of critical systems in aircraft. Fast and accurate fault detection and isolation (FDI) are necessary components for safe and reliable operation of the IDG and the aircraft. IDGs are complex systems, and a majority of the existing FDI techniques for the electrical subsystem (brushless generator) are based on signal analysis and heuristic methods derived from experience. Model-based fault diagnosis techniques are hypothesized to be more general and powerful in designing detection and isolation schemes. However, building sufficiently accurate models of brushless generators is a difficult task. dq models have been developed for single generators, but these models are not suitable to represent the complete brushless generator either in normal or fault situations, where the generator may become unbalanced. In this paper, we develop a novel hybrid dynamical model for the complete brushless ac generator. We exploit the hybrid modeling capability to accurately model different rectifier diode faults and rotor winding faults, reported as the most severe brushless generator faults. We simulate the hybrid model for nominal and different faulty conditions, and develop fault signatures for different machine faults.

Modern electrical power disribution systems play a critical role in system operations. Therefore, early fault detection and isolation is essential to maintaining system safety and avoiding catastrophic failures. This paper discusses a fault isolation scheme based on a qualitative fault signature-based isolation mechanism that applies to abrupt, incipient and intermittent faults in the system. We discuss the isolation algorithms for a combination of these faults, and demonstrate their performance on a set of test cases generated from a NASA Ames spacecraft power distribution testbed. Our results show good isolation accuracy with 103 out of 134 faulty scenarios isolated correctly. Most of the isolation errors can be attributed to errors in the detection scheme.

In this paper, we consider the detection problem with intermittent observations, due to the unreliable shared communication link between local sensors and the fusion center. Detection performance is analyzed using Neyman-Pearson criterion of maximizing the probability of detection, for a given probability of false alarm. The detector performance is compared, with and without intermittent observations, and a formal approach is presented to restore the original detector performance.

The design of wireless sensor networks for detection applications is a challenging task. On one hand, classical work on decentralized detection does not consider practical wireless sensor networks. On the other hand, practical sensor network design approaches that treat the signal processing and communication aspects of the sensor network separately result in sub optimal detection performance because network resources are not allocated efficiently. In this work, we attempt to cross the gap between theoretical decentralized detection work and practical sensor network implementations. We consider a cross-layer approach, where the quality of information, channel state information, and residual energy information are included in the design process of tree-topology sensor networks. The design objective is to specify which sensors should contribute to a given detection task, and to calculate the relevant communication parameters. We compare two design schemes: (1) direct transmission, where raw data are transmitted to the fusion center without compression, and (2) in-network processing, where data is quantized before transmission. For both schemes, we design the optimal transmission control policy that coordinates the communication between sensor nodes and the fusion center. We show the performance improvement for the proposed design schemes over the classical decoupled and maximum throughput design approaches.

Passive wireless sensors have emerged as a new technology to measure a vast majority of phenomena in our daily life. Passive sensors require no power source, and therefore their application domains are numerous, including health care, infrastructure protection, and national security, among many others. The deployment of wireless passive sensors and their readers has changed how detection needs to be performed. Passive sensors cannot pre-process the measurements as they have limited computational power. Therefore, no local decision is taken. Also, the reader polls the information from multiple sensors at the same time, and this causes collisions and hence packet drops and delays. In this paper, we formulate the detection performance, with non-ideal channels, in a probabilistic way, and compare with classical detection performance. We design an optimal adaptive Neyman-Pearson detector, given the channel probabilistic model, by formulating and solving a constrained optimization problem.

The decentralized detection performance, using wireless passive sensor networks, is analyzed according to the minimum probability of error criterion. Passive sensors communicate their measurements to the reader using data network packets, and therefore, the two main phenomena affecting the detection performance are packet loss and packet delay. In this paper, we formulate the decentralized detection problem with passive sensors and show that the optimal decision rule with packet loss is the likelihood ratio test. We present a comparative analysis study between detection with ideal and non-ideal channels, for the problem of DC level detection in White Gaussian Noise. We validate the analytical results using Monte Carlo Simulation study. Finally, we present a simple scheme for adaptive detector design, to restore the original detection performance, with the cost of increasing the delay for detection.

Integrated drive generators (IDGs) are the main source of electrical power for a number of critical systems in aircraft. Fast and accurate fault detection and isolation is a necessary component for safe and reliable operation of the IDG and the aircraft. IDGs are complex systems, and a majority of the existing fault detection and isolation techniques are based on signal analysis and heuristic methods derived from experience. Model-based fault diagnosis techniques are hypothesized to be more general and powerful in designing detection and isolation schemes, but building sufficiently accurate models of complex IDGs is a difficult task. dq0 models have been developed for design and control of generators, but these models are not suitable for fault situations, where the generator may become unbalanced. In this paper, we present a hybrid phase-domain model for the aircraft generator that accurately represents both nominal and parametric faulty behaviors. We present the details of the hybrid modeling approach and simulation results of nominal operation and fault behaviors associated with parametric faults in the aircraft generator. The simulation results show that the developed model is capable of accurately capturing the generator dynamics under a variety of normal and faulty configurations.