Safety Of Cyber-physical Systems Research Articles

Intent-Based Security for Functional Safety in Cyber-Physical Systems

We propose a novel intent-based method to prevent security attacks on the safety functions in cyber-physical systems used in smart manufacturing. Context information about a cyber-physical system is collected from various sensors including non-safety sensors measuring device temperature, motor rotation speed, or instantaneous power consumption of machines. Such contextual information along with operational and business intent of the system under consideration are then used to check whether the current situation is indeed an emergency situation or a normal situation. Unlike the conventional safety systems that only rely on raw sensor data and safety protocol status packets from safety sensors, which might be spoofed and/or modified, decision on the safety situation in our method is intelligently made by comparing aggregated sensor information from the cyber-physical system and its environment for compliance with pre-configured operational intents that define the normal safe and secure operation of the system. We also show how to integrate Machine Learning (ML) and Artificial Intelligence (AI) into the proposed method for efficient and automated analysis of both intents and aggregated context information to make more intelligent decisions in execution of functional safety protocols. Our proposed AI/ML integration approach also enables the prediction of safety critical situations before they occur. The proposed method aims to prevent unnecessary switching to fail-safe mode causing insecure system states such as emergency doors opening or system halting in normal situations. In addition, it prevents sticking in non-safe states and not switching to fail-safe mode in real emergencies which could cause hazard on device and/or people.

Modeling and Verification of Uncertain Cyber-Physical System Based on Decision Processes

Currently, there is uncertainty in the modeling techniques of cyber-physical systems (CPS) when faced with the multiple possibilities and distributions of complex system behavior. This uncertainty leads to the system’s inability to handle uncertain data correctly, resulting in lower reliability of the system model. Additionally, existing technologies struggle to verify the activity and safety of CPS after modeling, lacking a dynamic verification and analysis approach for uncertain CPS properties.This paper introduces a generalized possibility decision process as a system model. Firstly, the syntax and semantics of generalized possibility temporal logic with decision processes are defined. Uncertain CPS is extended by modeling it based on time-based differential equations and uncertainty hybrid time automaton. After that, model checking is performed on the properties of activity and safety using fuzzy linear time properties. Finally, a cold–hot hybrid constant-temperature system model is used for simulation experiments. By combining theory and experiments, this paper provides a new approach to the verification of uncertain CPS, effectively addressing the state explosion problem. It plays a crucial role in the design of uncertain CPS and offers a key solution for model checking in the presence of uncertainty.

A Timing-Based Framework for Designing Resilient Cyber-Physical Systems under Safety Constraint

Cyber-physical systems (CPS) are required to satisfy safety constraints in various application domains such as robotics, industrial manufacturing systems, and power systems. Faults and cyber attacks have been shown to cause safety violations, which can damage the system and endanger human lives. Resilient architectures have been proposed to ensure safety of CPS under such faults and attacks via methodologies including redundancy and restarting from safe operating conditions. The existing resilient architectures for CPS utilize different mechanisms to guarantee safety, and currently, there is no common framework to compare them. Moreover, the analysis and design undertaken for CPS employing one architecture is not readily extendable to another. In this article, we propose a timing-based framework for CPS employing various resilient architectures and develop a common methodology for safety analysis and computation of control policies and design parameters. Using the insight that the cyber subsystem operates in one out of a finite number of statuses, we first develop a hybrid system model that captures CPS adopting any of these architectures. Based on the hybrid system, we formulate the problem of joint computation of control policies and associated timing parameters for CPS to satisfy a given safety constraint and derive sufficient conditions for the solution. Utilizing the derived conditions, we provide an algorithm to compute control policies and timing parameters relevant to the employed architecture. We also note that our solution can be applied to a wide class of CPS with polynomial dynamics and also allows incorporation of new architectures. We verify our proposed framework by performing a case study on adaptive cruise control of vehicles.

Modeling Cyberattack Propagation and Impacts on Cyber-Physical System Safety: An Experiment

In this article, we present an experiment we conducted with discrete event simulations to analyze the effects of multi-step cyberattacks on the safety of cyber-physical systems. We show how to represent systems, their components (either software and/or hardware), communication links, security measures, and attacks from a malicious intruder. The latter are typically taken from the MITRE ATT&CK knowledge base. The discrete event simulation method makes it possible to represent any event affecting the system. We illustrate our approach by means of an illustrative example involving cyberattacks against the navigation system of an autonomous ship. We show how the formal modeling language AltaRica, primarily dedicated to safety analyses, can assess this illustrative example by representing the system and automatically extracting sequences of attacks, leading to a safety-critical situation, namely the deviation of the ship by the attacker. This article aims to discuss this approach and to outline the lessons learned from our experience.

Efficient Out-of-Distribution Detection Using Latent Space of β -VAE for Cyber-Physical Systems

Deep Neural Networks are actively being used in the design of autonomous Cyber-Physical Systems (CPSs). The advantage of these models is their ability to handle high-dimensional state-space and learn compact surrogate representations of the operational state spaces. However, the problem is that the sampled observations used for training the model may never cover the entire state space of the physical environment, and as a result, the system will likely operate in conditions that do not belong to the training distribution. These conditions that do not belong to training distribution are referred to as Out-of-Distribution (OOD). Detecting OOD conditions at runtime is critical for the safety of CPS. In addition, it is also desirable to identify the context or the feature(s) that are the source of OOD to select an appropriate control action to mitigate the consequences that may arise because of the OOD condition. In this article, we study this problem as a multi-labeled time series OOD detection problem over images, where the OOD is defined both sequentially across short time windows (change points) as well as across the training data distribution. A common approach to solving this problem is the use of multi-chained one-class classifiers. However, this approach is expensive for CPSs that have limited computational resources and require short inference times. Our contribution is an approach to design and train a single β -Variational Autoencoder detector with a partially disentangled latent space sensitive to variations in image features. We use the feature sensitive latent variables in the latent space to detect OOD images and identify the most likely feature(s) responsible for the OOD. We demonstrate our approach using an Autonomous Vehicle in the CARLA simulator and a real-world automotive dataset called nuImages.

Hybrid Modeling and Model Transformation of AADL for Verifying the Properties of CPS Space-Time Compositions

The wide application of Cyber Physical System (CPS) makes the security of CPS more and more concerned. As the key factors affecting the safety of CPS, space and time have also become the current research hotspot. The space and time safety of CPS requires that CPS arrives at the specified place at the specified time, time and space should meet the safety requirements of the CPS in the current CPS environment. We call the behavior space-time compositions. In order to solve the problem that CPS lacks the method of modeling and verification of space-time compositions, a hybrid Architecture Analysis & Design Language (AADL) modeling and model transformation method for CPS space-time compositions verification is proposed. Firstly, space-time description capability is extended in the AADL behavior annex and Hybrid AADL (HAADL) is proposed. Secondly, differential equations and space-time compositions vector are introduced in Process Algebra to propose Hybrid Space-Time Communication Sequential Processes (HS-TCSP). Furthermore, the Hybrid AADL is transformed to HS-TCSP. Finally, an example of an aircraft collision avoidance system is used to verify the effectiveness of the method.

Formal analysis of the continuous dynamics of cyber–physical systems using theorem proving

Transform methods, such as the Laplace and the Fourier transforms, are widely used for analyzing the continuous dynamics of the physical components of Cyber–physical Systems (CPS). Traditionally, the transform methods based analysis of CPS is conducted using paper-and-pencil proof methods, computer-based simulations or computer algebra systems. However, all these methods cannot capture the continuous aspects of physical systems in their true form and thus unable to provide a complete analysis, which poses a serious threat to the safety of CPS. To overcome these limitations, we propose to use higher-order-logic theorem proving to reason about the dynamical behavior of CPS, based on the Laplace and the Fourier transforms, which ensures the absolute accuracy of this analysis. For this purpose, this paper presents a higher-order-logic formalization of the Laplace and the Fourier transforms, including the verification of their classical properties and uniqueness. This formalization plays a vital role in formally verifying the solutions of differential equations in both the time and the frequency domain and thus facilitates formal dynamical analysis of CPS. For illustration, we formally analyze an industrial robot and an equalizer using the HOL Light theorem prover.

Attack Graph Implementation and Visualization for Cyber Physical Systems

Cyber-attacks threaten the safety of cyber physical systems (CPSs) as a result of the existence of weaknesses in the multiple structural units constituting them. In this paper, three cyber physical systems case studies of a pressurized water nuclear power plant (NPP), an industrial control system (ICS), and a vehicular network system (VNS) are examined, formally presented, and implemented utilizing Architecture Analysis and Design Language, determining system design, links, weaknesses, resources, potential attack instances, and their pre-and post-conditions. Then, the developed plant models are checked with a security property using JKind model checker embedded software. The attack graphs causing plants disruptions for the three applications are graphically visualized using a new graphical user interface (GUI) windows application.

Cyber-Physical Attack Detection and Recovery Based on RNN in Automotive Brake Systems

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literature. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and causes performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

Cyber Security Aspects of System for Time Synchronisation across National Communication Networks Using Cyber Physical Framework

A unique approach based on developments in machine learning techniques has been developed to rapidly and accurately identify frequency anomalies in atomic clocks. The results of the tests show that compared to the innovation technique and the extrapolation approach, the innovative method has a higher detection potential for micro frequency flaws and a faster discovery time. The problems with Indian UTC have also been discussed. The continual addition of a leap second to UTC is a significant negative. A Cyber-Physical System (CPS) has been proposed for the national timing system to identify a difference in atomic clocks. There have also been extensive expressions of worry on the safety of CPS. We have also discussed time allocation and the CPS model. During the study, eight different atomic clocks were considered and compared to one another. High levels of accuracy and precision were found in the developed model, and it was suggested that the 7 th and 8 th clocks have values of 0.9310 and 0.9643, while the recall and f1-scores are higher with values of 0.9643 and 0.9643, respectively. This indicates that results will vary in the LSTM based ML models of anomaly detection as epochs and noise levels vary.

The Safety Analysis: Disagreement of Wireless Communication-Based Consensus

This letter considers the recent and practically relevant problem of safety in cyber-physical systems (CPS). Practical applications of CPS can be designed through the use of wireless multi-agent systems (MAS) since they provide flexibility and interoperability of the system functions. On the other hand, the performance of wireless MAS is prone to jamming attacks and the destructive effects of wireless channels. The aim of this letter is to determine safety bounds on wireless CPS under jamming attacks in the presence of additive white Gaussian noise (AWGN) and wireless fading channels. The impact of both wireless channel impairments and jamming attacks on the performance of wireless MAS has been investigated for the first time. The disagreement vector, defined as the deviation from the desired consensus value of MAS based on the quantization error, is analyzed and investigated for AWGN and fading channels. Theoretical results are supported by matching simulation results.

Adaptive Threshold Generation for Fault Detection with High Dependability for Cyber-Physical Systems

Cyber-physical systems (CPS) applied to safety-critical or mission-critical domains require high dependability including safety, security, and reliability. However, the safety of CPS can be significantly threatened by increased security vulnerabilities and the lack of flexibility in accepting various normal environments or conditions. To enhance safety and security in CPS, a common and cost-effective strategy is to employ the model-based detection technique; however, detecting faults in practice is challenging due to model and environment uncertainties. In this paper, we present a novel generation method of the adaptive threshold required for providing dependability for the model-based fault detection system. In particular, we focus on statistical and information theoretic analysis to consider the model and environment uncertainties, and non-linear programming to determine an adaptive threshold as an equilibrium point in terms of adaptability and sensitivity. To do this, we assess the normality of the data obtained from real sensors, define performance measures representing the system requirements, and formulate the optimal threshold problem. In addition, in order to efficiently exploit the adaptive thresholds, we design the storage so that it is added to the basic structure of the model-based detection system. By executing the performance evaluation with various fault scenarios by varying intensities, duration and types of faults injected, we prove that the proposed method is well designed to cope with uncertainties. In particular, against noise faults, the proposed method shows nearly 100% accuracy, recall, and precision at each of the operation, regardless of the intensity and duration of faults. Under the constant faults, it achieves the accuracy from 85.4% to 100%, the recall of 100% from the lowest 54.2%, and the precision of 100%. It also gives the accuracy of 100% from the lowest 83.2%, the recall of 100% from the lowest 43.8%, and the precision of 100% against random faults. These results indicate that the proposed method achieves a significantly better performance than existing dynamic threshold methods. Consequently, an extensive performance evaluation demonstrates that the proposed method is able to accurately and reliably detect the faults and achieve high levels of adaptability and sensitivity, compared with other dynamic thresholds.

A simulation platform for human-machine interaction safety analysis of cyber-physical systems

Human-machine interaction (HMI) safety for cyber-physical systems (CPSs) is critical and its analysis is mandatory in many domains such as SCADA, autonomous cars, and medical devices. Generation of dynamic accident scenarios is the cornerstone of safety analysis. This paper presents a platform and associated methodology to effectively generate accident scenarios by modeling HMI errors using model-level fault injection, followed by simulation to produce dynamic evolutions of accident scenarios. An HMI fault tree template is proposed to guide the simulation. Results show that human mode confusion triggered by false displays may lead to severe accidents. Run-time assertions play a crucial role to detect HMI errors. A case study is described. The methodology expands Ptolemy II's modeling and simulation capabilities for CPS applications.

Rapid Integration of CPS Security and Safety

The security and safety of cyber-physical systems (CPSs) often influence each other. Ensuring that this does not have negative implications might require a large and rigorous effort during the development of CPS. However, early in the life-cycle, quick feedback can be valuable helping security and safety engineers to understand how seemingly trivial design choices in their domain may have unacceptable implications in the other. We propose the cyber risk assessment framework (CRAF) for this purpose. The CRAF is based on openly available and widely used taxonomies from the safety and security domains, and a unique mapping of where loss of data security may impact aspects of data with safety implications. This letter represents the first time these different elements have been brought together into a single framework with an associated process. Through examples from within our organizations we show how this framework can be put to good use.

An Adaptive Control Architecture for Mitigating Sensor and Actuator Attacks in Cyber-Physical Systems

Recent technological advances in communications and computation have spurred a broad interest in control law architectures involving the monitoring, coordination, integration, and operation of sensing, computing, and communication components that tightly interact with the physical processes that they control. These systems are known as cyber-physical systems and due to their use of open computation and communication platform architectures, controlled cyber-physical systems are vulnerable to adversarial attacks. In this technical note, we propose a novel adaptive control architecture for addressing security and safety in cyber-physical systems. Specifically, we develop an adaptive controller that guarantees uniform ultimate boundedness of the closed-loop dynamical system in the face of adversarial sensor and actuator attacks that are time-varying and partial asymptotic stability when the sensor and actuator attacks are time-invariant. Finally, we provide a numerical example to illustrate the efficacy of the proposed adaptive control architecture.

SFTA-Based Approach for Safety/Reliability Analysis of Operational Use-Cases in Cyber-Physical Systems

The software is often responsible for controlling the behavior of mechanical and electrical components, as well as interactions among these components in cyber-physical systems (CPS). The risks in CPS systems could result in losing tools, features, performance and even life. Therefore, safety analysis for software in these systems is a highly critical and serious issue. In general, safety and reliability approaches play a major role in a risk management process in CPS. In this paper, after reviewing the major techniques of software reliability and safety in CPS, an software fault tree analysis (SFTA)-based approach is presented for analysis of operational use-cases (UC) in a CPS system. In our approach, the events related to use-cases are extracted, and the related SFTA is then obtained using the proposed algorithm. Moreover, a semi-automatic method is presented in this paper to produce software failure mode and effects analysis (SFMEA) from SFTA. The results of our approach are applicable for software safety analysis in a real CPS system, including the control system of Iranian National Observatory telescope. Assessment of the suggested method is performed through numerous safety/reliability criteria and the qualitative/quantitative analysis based on these criteria.

Cyber Physical Systems & Public Utility in India: State of Art

The cyber physical system safety and security is one of the important and potential research directions recent days. Ongoing advances in science and engineering is going to improve the link between computational and physical elements by means of intelligent mechanisms, dramatically increasing the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber-physical systems. The transformation of physical systems into cyber-physical systems (CPS) by imbibing them with intelligence is an ongoing process that can substantially benefit the society and the environment by improving comfort, convenience and quality of life of the people, while reducing consumption of natural resources and reducing environmental footprint. Examples of cyber physical systems are Smart Grid Networks, Smart Transportation System, Enterprise Cloud Infrastructure, Utility Service Infrastructure for Smart Cities, etc. But, the inherent dangers in this transformation are: it allows flexible control and resource use; provides conduits for information leakage; prone to misconfigurations and deliberate attacks by outsiders and insiders. In this paper we discuss the state of art of cyber physical systems and smart utilities.

A Method for Cyber-Physical System Behavior Modeling and Safety Verification Based on Extended Hybrid System Description Language

The safety of Cyber-physical system(CPS) is up to its behavior, and it is a key property for CPS to be applied in critical application fields. A method for CPS behavior modeling and safety verification is put forward in this paper. The behavior model of CPS is described by extended hybrid system description language(EHYSDEL). The formal definition of hybrid program(HP) is given, and the behavior model is transformed to HP based on the definition. The safety of CPS is verified by inputting the HP to KeYmarea. The advantage of the approach is that it models CPS intuitively and verify it’s safety strictly avoiding the state space explosion