Large-scale Cyber-physical Systems Research Articles

Heterogeneous Data Aggregation and Normalization in Information Security Monitoring and Intrusion Detection Systems of Large-scale Industrial CPS

Monitoring of industrial cyber-physical systems (CPS) is an ongoing process necessary to ensure their security. The effectiveness of information security monitoring depends on the quality and speed of collection, processing, and analyzing of heterogeneous CPS data. Today, there are many methods of analysis for solving security problems of distributed industrial CPS. These methods have different requirements for the input data characteristics, but there are common features in them due to the subject area. The work is devoted to preliminary data processing for the security monitoring of industrial CPS in modern conditions. The general architecture defines the use of aggregation and normalization methods for data preprocessing. The work includes the issue from the requirements for the preprocessing system, the specifics of the subject area, to the general architecture and specific methods of multidimensional data aggregation.

A Dynamic Backoff Window Scheme for Machine-Type Communications in Cyber-Physical Systems

A cyber-physical system (CPS) requires a time-bounded communication process for real-time interaction between the digital world and the physical world so that they can form a tight loop. Using the machine-type communication (MTC) protocol for a large-scale CPS is a good solution. However, the MTC protocol uses a random access (RA) mechanism to solve the radio resource contention problem, but that inevitably causes access delays especially in scenarios when there is a large number of MTC devices. To meet CPS requirement and address this problem, we propose a dynamic backoff window adjustment (DBA) algorithm for a pull-based RA process that can appropriately distribute the failed MTC devices engaged in the backoff window, so as to expedite their contention retrial time interval and achieve increased success probability. Simulation results showed that the DBA algorithm significantly increases the success probability of MTC devices contending for RA, and as well, shortens the mean access delay of the MTC devices. In comparison to the standard RA backoff scheme and a novel dynamic backoff (DB) scheme, the DBA algorithm proved to be more effective and suitable for the CPS to the best of our knowledge.

Bayesian Spatiotemporal Gaussian Process for Short-term Load Forecasting Using Combined Transportation and Electricity Data

Smart cities can be viewed as large-scale Cyber-Physical Systems (CPS) where different sensors and devices record the cyber and physical indicators of the city systems. The collected data are used for improving urban life by offering services such as accurate electric load forecasting, and more efficient traffic management. Traditional monitoring for electricity and transportation networks generally do not provide full observability due to their limited coverage as well as high implementation and maintenance costs. For example, continuous traffic data collection is mostly limited to major highways only in big cities, whereas local roadways are usually covered once or twice a year. Also, there are no high-fidelity and real-time electric monitoring systems in all parts of power distribution networks. Combining the limited data from each of the urban systems together (e.g., electricity, transportation, environment, etc.) provides a better picture of the energy flow in a city. Furthermore, a city should be considered as a collection of the layers of tangled infrastructure networks, which connects people, places, and resources. Therefore, the study of traffic or electricity consumption forecasting should go beyond the transportation and electricity networks and merge with each other and even with other city networks such as environmental networks. As such, this article proposes a Bayesian spatiotemporal Gaussian Process model that employs the most informative spatiotemporal interdependency among different interconnected networks (in this case, electricity, transportation, and weather). The proposed load forecasting method is compared with other state-of-the-art methods using real-life data obtained from the City of Tallahassee in Florida. Results show that the proposed Bayesian spatiotemporal Gaussian Process model outperforms state-of-the-art methods.

An Augmented Lagrangian Approach for Distributed Robust Estimation in Large-Scale Systems

Nonlinear estimation using maximum-likelihood estimation and maximum a posterior probability approaches is frequently employed for large-scale cyber-physical and complex systems in the big data era. Efficient distributed processing and robust estimation algorithms resilient to outliers are of great importance. This paper proposes a novel method for distributed solution of these robust estimation problems with equality constraints based on the augmented Lagrangian method (ALM). Specifically, a novel covariance normalization method and an automatic method for selecting regularization parameter with improved performance are proposed. Under the ALM framework, nonlinear equality constraints and nonsmooth L 1 regularization can be incorporated. The proposed method is illustrated with two emerging applications respectively in robust distributed power system state estimation (DSSE) with nonlinear zero injection constraints and gene regulatory network (GRN) identification. Experimental results in DSSE show that the covariance normalization method improves considerably the convergence speed over the alternating direction method of multipliers algorithm and the robust statistics employed effectively mitigates the adverse effects of extreme outliers. Zero-injection constraints can be effectively incorporated. For GRN identification, putative genes and connectivity for a yeast dataset with 1.57 million variables can be identified via sparsity and piecewise temporal continuity penalties and they generally align well with literature.

Dynamic reliability modeling of cyber-physical edge computing network

ABSTRACT Recently, large scale cyber physical systems (LS-CPS) leverage network-cores provided by application providers (APs) to carry out analytics. These CPS-APs uses the automated cloud to gather traffic data streams, thereby reducing infrastructure and maintenance costs. However, network reliability and maintainability considering the arrival rate of user application requests presents a major challenge such as Edge-to-Fog and Fog-to-Cloud resource auto-scaling constraints. QoS dynamic reliability, as well as its flexible management, could offer an efficient network for CPS. In this paper, CloudMesh CPS architecture is presented as a promising solution to support services like Input-Output (IO) data stream, traffic engineering, service function optimization and software defined network monitoring in CPS IPv6 data-center core. Dynamic reliability modeling for QoS parameter scaling based on observable history is presented. Hence, QoS proactive auto-scaling algorithm (PASCQA) embedded with a heuristic predictor is introduced in CloudMesh CPS. IO streams are captured by the predictor which analyzes the QoS history for reliable global performance. Finally, the work realized the critical performance aspects of CPS architecture and analyzed the effects of QoS parameter configuration for real-time deployment. The implementation results show that CloudMesh offers satisfactory QoS metrics compared with Fat-tree-1 and Fat-tree-2 for CPS applications.

End-to-End Visual Domain Adaptation Network for Cross-Domain 3D CPS Data Retrieval

3D CPS (Cyber Physical System) data has been widely generated and utilized for multiple applications, e.g. autonomous driving, unmanned aerial vehicle and so on. For large-scale 3D CPS data analysis, 3D object retrieval plays a significant role for urban perception. In this paper, we propose an end-to-end domain adaptation framework for cross-domain 3D objects retrieval (C3DOR-Net), which learns a joint embedding space for 3D objects from different domains in an end-to-end manner. Specifically, we focus on the unsupervised case when 3D objects in the target domain are unlabeled. To better encode a 3D object, the proposed method learns multi-view visual features in a data-driven manner for 3D object representation. Then, the domain adaptation strategy is implemented to benefit both domain alignment and final classification. Specifically, an center-based discriminative feature learning method enables the domain invariant features with better intra-class compactness and inter-class separability. C3DOR-Net can achieve remarkable retrieval performances by maximizing the inter-class divergence and minimizing the intra-class divergence. We evaluate our method on two cross-domain protocols: 1) CAD-to-CAD object retrieval on two popular 3D datasets (NTU and PSB) in three designed cross-domain scenarios; 2) SHREC’19 monocular image based 3D object retrieval. Experimental results demonstrate that our method can significantly boost the cross-domain retrieval performances.

Separable and Localized System-Level Synthesis for Large-Scale Systems

A major challenge faced in the design of large-scale cyber-physical systems, such as power systems, the Internet of Things or intelligent transportation systems, is that traditional distributed optimal control methods do not scale gracefully, neither in controller synthesis nor in controller implementation, to systems composed of a large number (e.g., on the order of billions) of interacting subsystems. This paper shows that this challenge can now be addressed by leveraging the recently introduced system-level approach (SLA) to controller synthesis. In particular, in the context of the SLA, we define suitable notions of separability for control objective functions and system constraints such that the global optimization problem (or iterate update problems of a distributed optimization algorithm) can be decomposed into parallel subproblems. We then further show that if additional locality (i.e., sparsity) constraints are imposed, then these subproblems can be solved using local models and local decision variables. The SLA is essential to maintain the convexity of the aforementioned problems under locality constraints. As a consequence, the resulting synthesis methods have $O(1)$ complexity relative to the size of the global system. We further show that many optimal control problems of interest, such as (localized) LQR and LQG, $\mathcal {H}_2$ optimal control with joint actuator and sensor regularization, and (localized) mixed $\mathcal {H}_2/\mathcal {L}_1$ optimal control problems, satisfy these notions of separability, and use these problems to explore tradeoffs in performance, actuator, and sensing density, and average versus worst-case performance for a large-scale power inspired system.

Contract-Based Hierarchical Resilience Management for Cyber-Physical Systems

Orchestrated collaborative effort of physical and cyber components to satisfy given requirements is the central concept behind Cyber-Physical Systems (CPS). To duly ensure the performance of components, a software-based resilience manager is a flexible choice to detect and recover from faults quickly. However, a single resilience manager, placed at the centre of the system to deal with every fault, suffers from decision-making overburden; and therefore, is out of the question for distributed large-scale CPS. On the other hand, prompt detection of failures and efficient recovery from them are challenging for decentralised resilience managers. In this regard, we present a novel resilience management framework that utilises the concept of management hierarchy. System design contracts play a key role in this framework for prompt fault-detection and recovery. Besides the details of the framework, an Industry 4.0 related test case is presented in this article to provide further insights.

TORUS

Cyber-Physical Systems (CPS) contain intertwined and distributed software, hardware, and physical components to control complex physical processes. They find wide application in industrial systems, such as smart grid protection systems, which face increasingly complex communication and computation needs. Due to the scale and complexity of the interactions that occur within CPS, tracing requirements through to the system components and software code that implement them is often hard. Existing requirements management systems do not scale well, and traceability is difficult to implement and maintain in highly heterogeneous systems. However, the information trace that links provide is crucial for supporting testing and certification activities in safety-critical environments such as smart grids. The well-formed models of power systems provided by the IEC 61850 standard and the software design structure provided by the IEC 61499 Function Blocks standard can be leveraged to automate many traceability operations. We present Traceability of Requirements Using Splices (TORUS), a novel traceability framework for the development of large-scale safety-critical CPS. TORUS introduces splices , autonomous graph-based data structures that automatically create and manage trace links between requirements and components through the inevitable changes that occur during system development. The formal, graph-based structure of TORUS lends itself well to the development of sophisticated algorithms to automate the extraction of useful traceability information such as historical records and metrics for requirements coverage and component coupling. By capturing not only the current state of the system but also historical information, TORUS allows project teams to see a much richer view of the system and its artifacts. We apply TORUS to the development of a protection system for smart grid substations. In addition, through a number of experiments in splice creation, modification, and application of automated algorithms, we show that TORUS scales easily to large systems containing hundreds of thousands of requirements and system components and millions of possible trace links.

False Sequential Command Attack of Large-Scale Cyber-Physical Systems

Previous studies have demonstrated that false commands can cause severe damage to large-scale cyber-physical systems (CPSs). We focus on a kind of threat called false sequential command attack, with which attackers can generate false sequential commands, resulting in the illegal control of the physical process. We present a feasible attack model. Attackers delay the disaggregation of former commands by manipulating maliciously sub-controllers. Simultaneously, bad feedback data is injected to defeat the controller to issue latter commands. Thus, false command sequence is executed and the disruption of physical process can be obtained. It is also difficult for the detector to identify such attacks as injecting bad data. We also discuss other possible attack paths and analyze the corresponding disadvantages. Compared with other paths, the proposed model is more feasible and has more difficulties to be detected. A case study is given to validate the feasibility and effectiveness of proposed false sequential command attack model. Finally, we discuss the possible countermeasure.

Dynamic Risk Assessment and Analysis Framework for Large-Scale Cyber-Physical Systems

Cyberspace is growing at full tilt creating an amalgamation of disparate systems. This heterogeneity leads to increased system complexity and security flaws. It is crucial to understand and identify these flaws to prevent catastrophic events. However, the current state-of-the-art solutions are threa

Detectors on Edge Nodes against False Data Injection on Transmission Lines of Smart Grid

False data injection (FDI) attack is a hot topic in large-scale Cyber-Physical Systems (CPSs), which can cause bad state estimation of controllers. In this paper, we focus on FDI detection on transmission lines of the smart grid. We propose a novel and effective detection framework to identify FDI attacks. Different from the previous methods, there are multi-tier detectors which utilize edge nodes such as the programmable logic controllers (PLCs) instead of the central controller to detect attacks. The proposed framework can decrease the transmission time of data to reduce the latency of decisions because many sensory data need not be transmitted to the central controller for detection. We also develop a detection algorithm which utilizes classifiers based on machine learning to identify FDI. The training process is split from every edge node and is placed on the central node. The detectors are lightweight and are properly adopted in our detection framework. Our simulation experiments show that the proposed detection framework can provide better detection results than the existing detection approaches.

Radio resource management for data transmission in low power wide area networks integrated with large scale cyber physical systems

Recent advances in ICT technologies lead to intelligent services based on monitoring data. A system for the intelligent services is called as a cyber physical system (CPS). The CPS is an IoT-Cloud system which is an integrated system between computing world and a physical sensor field. It makes feasible applications by connecting the monitoring data in the physical world to decision making in the computing world. That is, between the physical world and the computing world, the CPS supports various interactions to objects as IoT-Cloud system. In the physical sensor field of the CPS, a sensor network, which consists of sensors and actuators is constructed. ZigBee is considered as the representative wireless communication technology for the sensor network. However, it has short transmission range and uses shared frequency band with high competition. Therefore, it is not suitable for the CPS applications with long service range such as smart factory or smart environment monitoring. For these CPS applications, it is required to apply low power wide area network (LPWAN) to the sensor network. In this paper, LPWAN requirements for the CPS (i.e., the IoT-Cloud system) applications are analyzed and a radio management method for data transmission of LPWAN in the IoT-Cloud system is designed. For the validation of the efficiency of the data transmission, computer simulations are carried out. Results show that the proposed method outperforms state of the art methods. In this way, the proposal improves the transmission delay over TDMA in between 33 and 51% depending on the number of sensors considered and presents a superior performance in the transmission of data packets.

Declarative Dependency Specification for Inter-connected Large-scale Cyber-physical Systems

Cyber-Physical Systems (CPS) are inter-connected software-hardware artefacts ranging from small-scale bio-electrical implants, medium-scale smart cars and intelligent production lines, to large-scale critical infrastructures with power grids, telecommunication networks and railway systems. Large-scale CPS plays a central role in modern society by delivering essential services for human daily life. Depending on the delivered services, any disruption or mal-function of these systems with a duration of hours or days will have severe societal and economic impacts and consequences, in particularly when the inter-dependencies between these systems are considered: the total consequences resulted by cascading effects is usually much larger than the one caused by the original failure. Federated simulation provides an effective means to analyse the overall dynamics of inter-connected large-scale CPS. In a typical federated simulation environment, dedicated domain-specific simulators, both commercial and open-source, are applied and inter-connected together to provide realistic system behaviours of the complete CPS networks. Specifying the dependencies among different simulator models is however a challenging task, especially for networks involving multiple large-scale CPS. In this paper, a declarative approach for dependency specification based on data stream processing is proposed. Preliminary results show the benefits comparing to the traditionally imperative approaches in terms of flexibility, scalability and usability.

Model-Free Fault Detection and Isolation in Large-Scale Cyber-Physical Systems

Detecting and isolating faults in cyber-physical systems (CPSs), e.g., critical infrastructures, smart buildings/cities, and the internet-of-things, are tasks that do generally scale badly with the CPS size. This work introduces a model-free fault detection and diagnosis system (FDDS) designed, having in mind scalability issues, so as to be able to detect and isolate faults in CPSs characterised by a large number of sensors. Following the model-free approach, the proposed FDDS learns the nominal fault-free conditions of the large-scale CPS autonomously by exploiting the temporal and spatial relationships existing among sensor data. The novelties in this paper reside in 1) a clustering method proposed to partition the large-scale CPS into groups of highly correlated sensors in order to grant scalability of the proposed FDDS, and 2) the design of model- and fault-free mechanisms to detect and isolate multiple sensor faults, and disambiguate between sensor faults and time variance of the physical phenomenon the cyber layer of CPS inspects.

Optimal Planning of Communication System of CPS for Distribution Network

IoT is the technical basis to realize the CPS (Cyber Physical System) for distribution networks, with which the complex system becomes more intelligent and controllable. Because of the multihop and self-organization characteristics, the large-scale heterogeneous CPS network becomes more difficult to plan. Using topological potential theory, one of typical big data analysis technologies, this paper proposed a novel optimal CPS planning model. Topological potential equalization is considered as the optimization objective function in heterogeneous CPS network with the constraints of communication requirements, physical infrastructures, and network reliability. An improved binary particle swarm optimization algorithm is proposed to solve this complex optimal problem. Two IEEE classic examples are adopted in the simulation, and the results show that, compared with benchmark algorithms, our proposed method can provide an effective topology optimization scheme to improve the network reliability and transmitting performance.

Modeling cyber-physical attacks based on probabilistic colored Petri nets and mixed-strategy game theory

Cyber-physical attacks are posing great threats to the safety and security of cyber-physical systems. Modeling cyber-physical attacks reasonably and efficiently is the basis for defending cyber-physical systems effectively, which requires the development of quantitative analysis and modeling approaches for expressing threat propagation in cyber-physical systems. This paper extends the colored Petri net model by defining a probabilistic colored Petri net model that comprises basic models, rules, logical operators and transitions that describe threat propagation between nodes. Basic cyber-physical attack models based on probabilistic colored Petri nets are presented. Furthermore, a systematic modeling approach is presented for constructing a quantitative cyber-physical attack model for a cyber-physical system. The weights of the cyber-physical attack model connections are computed using a mixed-strategy attack-defense game model for each node and solving the Nash equilibrium. Additionally, a hierarchical method of division and integration is proposed to efficiently model complex, large-scale cyber-physical systems. Finally, the systematic cyber-physical attack modeling approach is applied to a case study involving a thermal power plant.

Situation awareness and computational intelligence in opportunistic networks to support the data transmission of urban sensing applications

Smart cities can be seen as large-scale Cyber-Physical Systems with sensors monitoring cyber and physical indicators and with actuators dynamically changing the complex urban environment in some way. In this context, urban sensing is a new paradigm that exploits human-carried or vehicle-mounted sensors to ubiquitously collect data to provide a holistic view of the city. A challenge in this scenario is the transmission of sensed data in situations where the networking infrastructure is intermittent or unavailable. This paper outlines our research into an engine that uses opportunistic networks to support the data transmission of urban sensing applications. It applies situation awareness and computational intelligence approaches to perform routing, adaptation, and decision-making procedures. We carried out simulations within a simulated environment that showed our engine had 12% less overhead than other compared approaches.

Zen-ReqOptimizer: a search-based approach for requirements assignment optimization

At early phases of a product development lifecycle of large scale Cyber-Physical Systems (CPSs), a large number of requirements need to be assigned to stakeholders from different organizations or departments of the same organization for review, clarification and checking their conformance to standards and regulations. These requirements have various characteristics such as extents of importance to the organization, complexity, and dependencies between each other, thereby requiring different effort (workload) to review and clarify. While working with our industrial partners in the domain of CPSs, we discovered an optimization problem, where an optimal solution is required for assigning requirements to various stakeholders by maximizing their familiarity to assigned requirements, meanwhile balancing the overall workload of each stakeholder. In this direction, we propose a fitness function that takes into account all the above-mentioned factors to guide a search algorithm to find an optimal solution. As a pilot experiment, we first investigated four commonly applied search algorithms (i.e., GA, (1 + 1) EA, AVM, RS) together with the proposed fitness function and results show that (1 + 1) EA performs significantly better than the other algorithms. Since our optimization problem is multi-objective, we further empirically evaluated the performance of the fitness function with six multi-objective search algorithms (CellDE, MOCell, NSGA-II, PAES, SMPSO, SPEA2) together with (1 + 1) EA (the best in the pilot study) and RS (as the baseline) in terms of finding an optimal solution using an real-world case study and 120 artificial problems of varying complexity. Results show that both for the real-world case study and the artificial problems (1 + 1) EA achieved the best performance for each single objective and NSGA-II achieved the best performance for the overall fitness. NSGA-II has the ability to solve a wide range of problems without having their performance degraded significantly and (1 + 1) EA is not fit for problems with less than 250 requirements Therefore we recommend that, if a project manager is interested in a particular objective then (1 + 1) EA should be used; otherwise, NSGA-II should be applied to obtain optimal solutions when putting the overall fitness as the first priority.

Stability of Agent Based Distributed Model Predictive Control Over a Lossy Network

In this paper, an agent based control formulation of a large-scale cyber-physical system is proposed. Each agent can partially observe a part of the global dynamical process and estimate the associated local states through a combination of traditional Kalman filtering algorithm and consensus. The local estimates are then used for state feedback control. The optimal feedback gain of individual agents is obtained through dynamically solving a moving horizon linear quadratic optimization problem. The agents also exchange information among neighbors to coordinate the agent-wise state-feedback controls. Finally, a control decision incurring the least cost among all agents is applied to the global system. A Lyapunov function-based stability analysis is performed to obtain a bound over the degree of agent negotiation in designing the control decision. Besides, the effect of lossy communication network in control design and henceforth in global system stability is also investigated and the corresponding bound in control consensus is obtained. The theoretical results are verified via simulation of a 10-agent and a 50-agent dynamical process within a radial topology.