Networked Cyber-physical Systems Research Articles

Distributed Interval Observers with Switching Topology Design for Cyber-Physical Systems

In this paper, the distributed interval estimation problem for networked Cyber-Physical systems suffering from both disturbances and noise is investigated. In the distributed interval observers, there are some connected interval observers built for the corresponding subsystems. Then, due to the communication burden in Cyber-Physical systems, we consider the case where the communication among distributed interval observers is switching topology. A novel approach that combines L∞ methodology with reachable set analysis is proposed to design distributed interval observers. Finally, the performance of the proposed distributed interval observers with switching topology is verified through a simulation example.

Model-based impact analysis in dynamic System of Systems

Cyber-Physical Systems (CPS) are increasingly integrated into System of Systems (SoS). Each CPS needs to be resilient to changes. A special example is built by networked CPS forming a Cyber-Physical Production System (CPPS), like robotic arms and manufacturing cells. Changes in this dynamic SoS, such as variations in downstream manufacturing processes, can affect functionalities of the engineered CPS. Methods, models and tools from Systems Engineering can help system architects to react quickly and accurately to changes in the operation phase and assess the impacts. Currently, there are no specific approaches for assessing change impacts for a CPS in a SoS. In this paper, model-based impact analysis functionalities for a SoS are developed in a six-step research approach: Based on a systematic literature review, current approaches are identified and analysed (1). Success factors for adaptations in SoS are derived (2) and the approaches are evaluated in a matrix (3). The most suitable approach is adapted and implemented in system models (4). It is applied to the CPPS operated at the Heinz Nixdorf Institute as a case example (5) to evaluate the adapted approach based on success factors (6). The approach enables system architects to assess impacts of changes on systems in a SoS.

Cybersecurity for autonomous vehicles against malware attacks in smart-cities

Smart Autonomous Vehicles (AVSs) are networks of Cyber-Physical Systems (CPSs) in which they wirelessly communicate with other CPSs sub-systems (e.g., smart -vehicles and smart-devices) to efficiently and securely plan safe travel. Due to unreliable wireless communication among them, such vehicles are an easy target of malware attacks that may compromise vehicles’ autonomy, increase inter-vehicle communication latency, and drain vehicles’ power. Such compromises may result in traffic congestion, threaten the safety of passengers, and can result in financial loss. Therefore, real-time detection of such attacks is key to the safe smart transportation and Intelligent Transport Systems (ITSs). Current approaches either employ static analysis or dynamic analysis techniques to detect such attacks. However, these approaches may not detect malware in real-time because of zero-day attacks and huge computational resources. Therefore, we introduce a hybrid approach that combines the strength of both analyses to efficiently detect malware for the privacy of smart-cities.

Model Based Verification of Spiking Neural Networks in Cyber Physical Systems

Spiking Neural Networks (SNNs) have found increasing utility in designing safety-critical Cyber-Physical Systems (CPSs) such as implantable medical devices, autonomous vehicles, and space robotics due to their capability to operate on information represented in temporal coding and exhibit various behavioural modalities. Thus, there has been recent interest in formally verifying their timing behaviours and providing soundness guarantees of their diverse characteristics. However, beyond the simplistic Leaky Integrate and Fire (LIF) model, which only mimics 3 spiking behaviours, there is a lack of unifying methodology in literature to verify complex dynamics of biological neurons exhibiting 20 spiking behaviours as demonstrated by the pioneering work of Izhikevich. There is also a complete lack of formulation for the verification of SNN-based systems. This paper bridges these gaps by proposing a model-based approach for designing SNN-based controllers in CPS. We propose sound structural transformations translating any spiking neuron into networks of Timed Automata (TA), model the complex Izhikevich neural model and formally verify all 20 timing behaviours it exhibits for the first time. We then present two case studies that were modelled as SNNs using our approach: the PID controller, and the Car-Following controller, and subsequently attempt static model checking and statistical verification of their generated TA models for safety guarantees.

Model-Free Control in Wireless Cyber-Physical System With Communication Latency: A DRL Method With Improved Experience Replay.

This article explores the model-free remote control problem in a wireless networked cyber-physical system (CPS) composed of spatially distributed sensors, controllers, and actuators. The sensors sample the states of the controlled system to generate control instructions at the remote controller, while the actuators maintain the system's stability by executing control commands. To realize the control under a model-free system, the deep deterministic policy gradient (DDPG) algorithm is adopted in the controller to enable model-free control. Unlike the traditional DDPG algorithm, which only takes the system state as input, this article incorporates historical action information as input to extract more information and achieve precise control in the case of communication latency. Additionally, in the experience replay mechanism of the DDPG algorithm, we incorporate the reward into the prioritized experience replay (PER) approach. According to the simulation results, the proposed sampling policy improves the convergence rate by determining the sampling probability of transitions based on the joint consideration of temporal difference (TD) error and reward.

Secured data transmissions in corporeal unmanned device to device using machine learning algorithm

Cyber–physical systems (CPS) for device-to-device (D2D) communications are gaining prominence in today’s sophisticated data transmission infrastructures. This research intends to develop a new model for UAV transmissions across distinct network nodes, which is necessary since an automatic monitoring system is required to enhance the current D2D application infrastructure. The real time significance of proposed UAV for D2D communications can be observed during data transmission state where individual data will have huge impact on maximizing the D2D security. Additionally, through the use of simulation, an exploratory persistence tool is offered for CPS networks with fully characterized energy issues. This UAV CPS paradigm is based on mobility nodes, which host concurrent systems and control algorithms. In sixth-generation networks, when there are no barriers and the collision rate is low and the connectivity is fast, the method is also feasible. Unmanned aerial vehicles (UAVs) can now cover great distances, even while encountering hazardous obstacles. When compared to the preexisting models, the simulated values for autonomous, collision, and parametric reliability are much better by an average of 87%. The proposed model, however, is shown to be highly independent and exhibits stable perceptual behaviour. The proposed UAV approach is optimal for real-time applications due to its potential for more secure operation via a variety of different communication modules.

Adaptive Optimization With Decaying Periodic Dither Signals

Real-time optimization in cyber–physical network systems with unknown system parameters must integrate optimization and parameter estimation, leading to adaptive optimization problems. Such problems encounter fundamental conflict between optimization and system identifiability. Recently, a new method of employing a stochastic or periodic dither has been introduced to resolve this conflict and achieve convergence toward optimal solutions. However, adding a dither introduces persistent disturbances to the optimal solution, resulting in an essential tradeoff between convergence rate and steady-state error. This article introduces a method of adding decaying periodic dither signals into the cyber–physical system, which can still provide sufficient excitations for estimating the unknown parameters and, at the same time, asymptotically reduce optimality errors down to zero without affecting negatively on identifiability. The convergence properties of parameter estimation and optimization updates are provided simultaneously, first for noise-free and model uncertain free cases, followed by general systems that include observation noise and modeling errors. A simulation example is used to illustrate the adaptive optimization algorithms and the main properties.

Output based resilient model predictive control of Cyber–Physical Systems under cover attacks

In this article, a state estimate based robust model predictive control strategy is developed for networked cyberphysical systems under false data injections. The core of the proposed approach consists in elaborating an observed based general framework whose main feature relies on its adaptability to different classes of attacks. The main feature of the proposed scheme relies on the capability to mitigate undesired system behaviors due to external malicious actions. This is achieved by showing that an augmented state-estimate/estimation error set-membership conditions allow us to quickly detect data integrity anomalies and, as a consequence, to implement adequate counter-measures. In particular, set-theoretic model predictive arguments are combined with the perturbation analysis and sequential quadratic programming in order to reduce as much as possible the occurrence of refresh procedures on the communication network when resilient command actions are no longer available. As one of its main merits, these arguments allow to remove the constructive assumptions present in very recent competitors. In this respect, the framework is customized for covert attacks by specifying actuation and detection phases and proving feasibility and closed-loop stability properties. The core of the resulting solution exploits output feedback receding horizon and set-theoretic control ideas so that the resilient nature of the virtual control moves is formally put in light.

An integrated outlook of Cyber–Physical Systems for Industry 4.0: Topical practices, architecture, and applications

Industry 4.0 requires a strong understanding of Cyber-Physical systems (CPS). An Industry 4.0-enabled manufacturing environment that offers real-time data gathering, transparency and analysis across all parts of a manufacturing process is known as cyber–physical systems, also known as cyber manufacturing. Data analytics enables executives to make data-driven choices and boost productivity, while automation speeds up manufacturing and decreases machine downtime. The main objective of deploying Industry 4.0 solutions is to enable manufacturing organisations to increase collaboration by making the correct information accessible to the right people in real time. The aim of encouraging optimal decision-making at the appropriate moment is to improve efficiency and production further. The critical terms in Industry 4.0, such as the CPS, Internet of Things (IoT), and Digital Twin, are widely used interchangeably in conversations about smart manufacturing. These are essential to Industry 4.0 and smart manufacturing because they give users access to real-time operating data of the equipment they represent. Cyber components and physical components make up the two elements of CPS. The main aim of this paper is to brief CPS and its need for Industry 4.0. Embedded Processes and Smart 5C diagrammatically elaborate architecture of CPS for Industry 4.0. Finally, the paper identifies and discusses significant applications of CPS in Industry 4.0. For this paper, we identified and then studied relevant literature on CPS for establishing an Industry 4.0 environment. CPS is integrated into several items, including vehicles and other equipment, to carry out particular functions. CPS can be utilised in any industry, including engineering, manufacturing, transportation, and even health care, because they are all easy to use. CPS network collaborative systems are built for communication. Furthermore, cutting-edge network technologies like cloud solutions are used by CPS. • Industry 4.0 requires a strong understanding of Cyber-Physical systems (CPS). • Industry 4.0 enable manufacturing increase collaboration through CPS. • This paper brief about CPS and its need for Industry 4.0. • Embedded Processes and Smart 5C Architecture of CPS for Industry 4.0 are elaborated. • Finally, paper identifies and discusses significant applications of CPS for Industry 4.0.

Mixed Strategy Analysis in Attack-Defense Game Model Based on 5G Heterogeneous Network of CPS Using ncPSO

The development of the 5th Generation Mobile Communication Technology not only brings convenience to people but also brings many network security problems. Based on the static game theory of complete information, a game model of attack and defense with limited resources in heterogeneous networks of Cyber Physical Systems is established. This model analyzes the basic rules of the offensive and defensive strategies of both parties when the offensive and defensive resources are limited in the 5th Generation Mobile Communication Technology network environment. The model can also describe the interaction between attackers and defenders. A novel compact particle swarm optimization algorithm is proposed to solve the difficult problem of solving the Nash equilibrium of this game model. The simulation experiment proves that novel compact particle swarm optimization algorithm has good optimization ability and shows that the algorithm can effectively solve the Nash equilibrium of the model. The simulation experiment provides a strategic reference for the attack-defense game with limited resources.

An enhanced encryption-based security framework in the CPS Cloud

The rapid advancement of computation techniques and cloud computing has led to substantial advancements in Cyber-Physical Systems (CPS), particularly in the field of health care. There are a variety of ways in which CPS is used in healthcare today, including delivering intelligent feedback systems, automatically updating patient data digitally, monitoring patients passively with biosensors, etc. In recent years, cyber-physical systems have become capable of making lifesaving decisions as they are becoming more connected to the cloud. However, healthcare has become one of the most critical issues for many. A CPS network uses the Internet of Medical Things (IoMT) to continuously monitor patients’ health metrics such as body temperature, heart rate, etc. Due to physical connectivity restrictions, networks are more susceptible to security threats. In spite of the fact that the data is stored in the cloud, it is necessary to provide security regardless of device security and network security. Several cyber-security vulnerabilities have been identified in cloud-based healthcare systems in particular. To give patients a reliable healthcare experience, security concerns with CPSs need to be addressed carefully. In this context, this paper proposes a Cross-Breed Blowfish and MD5 (CBM) approach to improve the security of health data in the CPS cloud. The proposed model uses the wireless sensor network, in which data acquired by the network is transmitted via the transmitting node. Using the fuzzified effective trust-based routing protocol (FET-RP), the most efficient path for data travel is selected. The best route is determined using Butter-Ant Optimization (BAO) algorithm. The proposed method conveys data throughput encryption and decryption in a decoded format. The encrypted data is then stored in the cloud database for security reasons. The route finding algorithm is the one which is sending the data from one end to other end. The data is encrypted based on the source and destination. We compare the performance metrics of our recommended technique to those of other existing techniques, such as RSA, Two fish, ICC, and FHEA, in order to ensure that it performs optimally. The values of Cross Breed Blowfish and MD5 and FET-RP with regard to the performance metrics in terms of encryption (60 ms), decryption (55 ms), latency (60 s), throughput (97 mbps), security level (98%), and execution time (57 ms) which outperforms the conventional methods by 10–15%. Also the proposed encryption shows the considerable improvement in the level of security making our model a real world solution.

Modeling and fault diagnosis of distribution networks cyber physical system based on IEC61850

In order to improve the fault tolerance and solution efficiency of power grid fault diagnosis model, a distribution network cyber physical system (CPS) fault diagnosis model based on advanced digital power system simulator (ADPSS) and IEC61850 is proposed. Firstly, the action rules and action status of CPS fault mode of distribution network from the whole correlation of fault element, protection action and circuit breaker trip, and the CPS grid topology of distribution network is constructed in ADPSS based on the topology and component characteristics of system. Secondly, according to the IEC61850 unified information model and modeling rules for the protection function of system equipment and operation function of circuit breaker, an information model with alarm, measurement, protection and fault recording logic is established. Then, the action status is obtained and used for the establishment of the distribution network fault diagnosis model by parsing the GOOSE message. Finally, the proposed modeling method and fault diagnosis strategy is validated through the test case.

Optimal Control of PC-PLC Virus-Mutation and Multi-Delay Propagation Model in Distribution Network CPS

The intelligent manufacturing of power systems has led to many challenges. The cyber-physical system (CPS) was introduced to solve the problem of insufficient integration of equipment and systems. It brings advantages, but also risks. In the distribution network CPS, malicious attacks on the PC-PLC communication network can cause significant incidents and affect system safety. The paper discusses two challenges, of possible mutated virus attacks and multi-delay in the PC-PLC coupled network. We present for the first time a virus-mutation and multi-delay propagation model. Then, to effectively control the virus propagation in the network and minimize the cost, the paper proposes five control measures, introduces their possible control combinations, and solves the optimal control problem with the Pontryagin maximum theorem. Finally, simulations verify the optimal control strategies for all combinations. By comparing the effects of maximum control, minimum control, average control, and optimal control, the optimal control strategy has been proven to be effective.

Research on Attack Detection of Cyber Physical Systems Based on Improved Support Vector Machine

Cyber physical systems (CPS), in the event of a cyber attack, can have a serious impact on the operating physical equipment. In order to improve the attack detection capability of CPS, an support vector machine (SVM) attacks detection model based on particle swarm optimization (PSO) is proposed. First, the box plot anomaly detection method is used to detect the characteristic variables, and the characteristic variables with abnormal distribution are discretized. Secondly, the number of attack samples was increased by the SMOTE method to solve the problem of data imbalance, and the linear combination of characteristic variables was performed on the high-dimensional CPS network traffic data using principal component analysis (PCA). Then, the penalty coefficient and the hyperparameter of the kernel function in the SVM model are optimized by the PSO algorithm. Finally, Experiments on attack detection of CPS network traffic data show that the proposed model can detect different types of attack data and has higher detection accuracy compared with general detection models.

Output feedback stabilization of large-scale networked cyberphysical systems using cryptographic techniques

This paper aims to create a secure environment for state estimation and control design of a networked system composed of multiple dynamic entities and remote computational units, in the presence of disclosure attacks. In particular, both dynamic entities and computational units may be vulnerable to attacks and become malicious. The objective is to ensure that the input and output data of the benign entities are protected from the malicious parties as well as protected when they are transmitted over the network in a distributed environment. We propose a methodology integrating a novel double-layer cryptographic technique with an observer-based control algorithm to achieve the objective where the cryptographic technique addresses the security requirements and the control algorithm satisfies the performance requirements.

SafeSoCPS: A Composite Safety Analysis Approach for System of Cyber-Physical Systems.

The System of Cyber-Physical Systems (SoCPS) comprises several independent Cyber-Physical Systems (CPSs) that interact with each other to achieve a common mission that the individual systems cannot achieve on their own. SoCPS are rapidly gaining attention in various domains, e.g., manufacturing, automotive, avionics, healthcare, transportation, and more. SoCPS are extremely large, complex, and safety-critical. As these systems are safety-critical in nature, it is necessary to provide an adequate safety analysis mechanism for these collaborative SoCPS so that the whole network of these CPSs work safely. This safety mechanism must include composite safety analysis for a network of collaborative CPS as a whole. However, existing safety analysis techniques are not built for analyzing safety for dynamically forming networks of CPS. This paper introduces a composite safety analysis approach called SafeSoCPS to analyze hazards for a network of SoCPS. In SafeSoCPS, we analyze potential hazards for the whole network of CPS and trace the faults among participating systems through a fault propagation graph. We developed a tool called SoCPSTracer to support the SafeSoCPS approach. Human Rescue Robot System—a collaborative system—is taken as a case study to validate our proposed approach. The result shows that the SafeSoCPS approach enables us to identify 18 percent more general faults and 63 percent more interaction-related faults in a network of a SoCPS.

Securing Smart City Services in Cyber-Physical Systems Using the Computation Annealed Selection Process

Cyber-physical systems in a smart city environment offer secure computations in addition to robust resources and secure information exchanges. From the security aspect, the selection of legitimate computing resources is considered the most trustworthy measure for preserving user and data privacy. However, the outlier information results in false computations and time-consuming privacy measures for smart city users. This article introduces the Computation Annealed Selection Process (CASP) for combating outlier information in cyber-physical system networks. This process is assimilated with different infrastructure units in the smart city in an adaptable fashion. The adaptive measure administers information and user privacy through mutual time-key-based authentication. Information privacy is endured until the smart city provider provides a valid service interval. W2The service interval is evaluated for its consistency in maintaining the privacy and is validated using decision-tree learning. The decision tree performs interval classification and key assignment recommendations. Based on the decisions, the privacy is either prolonged or withdrawn for the information exchanged between the users and service providers. This process is repeated until the end of the service interval, identifying the outlier information in legitimate intervals. The proposed process improves detection, legitimacy rate, interval time, and interval rate by 7.57%, 10.4%, 13.15%, and 7.59%, respectively.

Cyber-Physical Systems and their Security Issues

Broadly, cyber physical systems are systems that integrate computing into the physical world. Control systems, the increase in their intersection with information technology networks and the variety of communication methods also cause the threat vectors in cyber physical systems security to change and increase. This shows that traditional information technology security measures, new generation information technology security solutions or security solutions specific to control systems will be insufficient alone, and the need for integrated security solutions that include all components in the cyber physical systems network and include operational scenarios. In this work, it was cover a couple of definitions of cyber physical systems and it was present an overview of cyber physical systems, what they are, where they are found, and how they are used. It also includes some information on the specific reasons cyber physical systems present greater security demands. The stakes are higher, the components are much more difficult to maintain and update, and the systems are more complex. The goal is to understand these systems well enough to design effective security measures to counter potential attacks.

Resilient Model Predictive Control for Constrained Cyber-Physical Systems Subject to Severe Attacks on the Communication Channels

In this article, a robust model predictive control strategy is developed for networked cyber-physical systems under false data injections. The main feature of the proposed scheme relies on the capability to mitigate undesired system behaviors due to external malicious actions. This is achieved by showing that set-membership conditions allow us to quickly detect data integrity anomalies and, as a consequence, to implement adequate countermeasures. The core of the resulting solution exploits receding horizon and set-theoretic control ideas so that the resilient nature of the virtual control moves is formally put in light. Simulations on a <inline-formula><tex-math notation="LaTeX">$B747-100/200$</tex-math></inline-formula> aircraft model show effectiveness and merits of the proposed resilient control architecture.

Novel Hybrid Model for Intrusion Prediction on Cyber Physical Systems’ Communication Networks based on Bio-inspired Deep Neural Network Structure

There are growing concerns on the security of communication networks of Cyber Physical Systems (CPSs). In a typical Cyber Physical System (CPS), the plant, actuators, sensors and controller interface through a communication network, which enable computing and data transmission in the CPS. Consequently, the communication network is vulnerable to sophisticated attacks. Attacks on CPSs communication networks can cause damage to critical resources and infrastructure. In this sense, it is crucial to accurately predict these attacks in order to minimise their impact on the target CPSs networks. In this paper, we propose a novel hybrid approach for intrusion prediction on CPSs communication networks. We use a bio-inspired hyperparameter search technique to generate an improved deep neural network structure based on the core hyperparameters of a neural network. Furthermore, we derive a prediction model based on the improved neural network structure and evaluate its performance using two well-known datasets, namely, the CICIDS2017 and UNSW-NB15 datasets. Results obtained from rigorous experimentation show that our model can predict diverse attack types with high accuracy, low error and false positive rates, and outperforms state-of-the-art comparative models.