Large-scale Cyber-physical Systems Research Articles

Adaptive Decentralized Finite-Time Fuzzy Secure Control for Uncertain Nonlinear CPSs Under Deception Attacks

This article addresses the adaptive secure control problem for a class of uncertain nonlinear large-scale cyber-physical systems (CPSs) subjected to deception attacks. Specifically, a novel adaptive fuzzy control scheme for each subsystem of CPSs is designed to mitigate the effects of cyber-attacks that intentionally tamper with control signals from controllers to actuators and the state signals from sensors to controllers, and a Nussbaum function is proposed to tackle unknown time-varying control directions. Moreover, a finite-time converging secure control scheme is designed for compromised CPSs to guarantee all the states converge to a predetermined small set in finite time. It is shown that with the proposed control scheme, all the signals of the closed-loop system are proved to be semi-globally bounded. Two simulation examples are provided to illustrate the effectiveness of the proposed secure control method.

Cloud-Based Underactuated Resilient Control for Cyber-Physical Systems Under Actuator Attacks

Cyber attacks threaten the security of cyber-physical systems (CPSs) seriously. Resilient control has been studied to defend cyber attacks. However, existing resilient control schemes have not considered system structure changes caused by actuator attacks. Such structure changes are more destructive and harmful than the actuator attack scenarios investigated in the literature, demanding new resilient control strategies. They will be addressed in this paper in cloud computing environments, which are increasingly deployed in large-scale CPSs. More specifically, a resilient control scheme is designed which consists of two controllers: a local resilient controller and a cloud-based resilient controller. The local resilient controller withstands actuator attacks that simply tampers the actuator output to a large extent. The cloud-based resilient controller aims to resist the actuator attacks that destroy the system structure. Simulations are conducted on a permanent synchronous motor control system to demonstrate the proposed resilient control scheme.

Self-adaptive end-to-end resource management for real-time monitoring in cyber–physical systems

This paper proposes a novel self-adaptive resource management framework that preserves a low end-to-end monitoring delay in large-scale cyber–physical systems (CPS) while providing high monitoring resolution. According to the tradeoff relationship between the end-to-end delay and monitoring resolution, our proposed algorithm is designed inspired by Lyapunov optimization framework that is mathematically time-average utility optimal under delay/stability constraints. Our proposed Lyapunov optimization-based framework dynamically controls both sensing period on sensor nodes and analysis period on back-end server nodes based on the workload of network and CPU resources. Moreover, our framework does not require additional messages between sensor and server nodes. In our framework, each node autonomously recognizes the underuse or overuse of network and CPU resources with only local state information and self-adapts periods of sensing and analysis under Lyapunov optimization theory. Furthermore, our framework provides different priorities for classes of sensing data and adaptively limits periods of sensing and analysis of low-priority classes. Our proposed framework is designed and implemented on a well-known publish/subscribe (pub/sub) network protocol, Message Queuing Telemetry Transport (MQTT), and finally we can confirm that our proposed framework achieves low end-to-end monitoring delays and high monitoring resolutions.

Reinforcement Learning of Structured Stabilizing Control for Linear Systems With Unknown State Matrix

This article delves into designing stabilizing feedback control gains for continuous-time linear systems with unknown state matrix, in which the control gain is subjected to a structural constraint. We bring forth the ideas from reinforcement learning (RL) in conjunction with sufficient stability and performance guarantees in order to design these structured gains using the trajectory measurements of states and controls. We first formulate a model-based linear quadratic regulator (LQR) framework to compute the structured control gain. Subsequently, we transform this model-based LQR formulation into a data-driven RL algorithm to remove the need for knowing the system state matrix. Theoretical guarantees are provided for the stability of the closed-loop system and the convergence of the structured RL (SRL) algorithm. A remarkable application of the proposed SRL framework is in designing distributed static feedback control, which is necessary for automatic control of many large-scale cyber–physical systems. As such, we validate our theoretical results with numerical simulations on a multiagent networked linear time-invariant dynamical system.

Learning Spatial Graph Structure for Multivariate KPI Anomaly Detection in Large-Scale Cyber-Physical Systems

Anomaly detection on multivariate Key Performance Indicators (KPIs) is a key procedure for the quality and reliability of large-scale Cyber-Physical Systems (CPSs). Although extensive efforts have been paid in learning normal data distributions, the spatial dependence of different dimensional KPIs is barely explored to reasonably represent the complexity and time-varying nature of systems. In this paper, we propose to model the spatial dependence of multivariate KPI by combining a more reasonable graph learning method with a graph attention mechanism to obtain the complex spatial dependence in an unsupervised manner. First, we transform the multivariate KPI into graph structures with a specially designed KPI graph learning module. Second, the Graph Attention mechanism extracts the spatial dependence in the KPI graphs. Finally, our method jointly trains forecasting-based model and reconstruction-based model to detect anomalies. Through a large number of related experiments on four real-world datasets, we demonstrate the feasibility of our method and the F1-Score improves by 9% over the baseline model. Further analysis shows that the graph learning method in this paper can more reasonably describe the dependence between multivariate KPI, and the graph attention mechanism can more accurately capture the correlation between them, which is helpful for fault diagnosis.

Distributed Resilient State Estimation for Cyber-Physical Systems Against Bit Errors: A Zonotopic Set-Membership Approach

This paper addresses the distributed resilient state estimation issue for a class of large-scale cyber-physical systems (CPSs) against bit errors. Owing to the reliability of the binary data, a novel binary encoding-decoding scheme is developed to enable digital communication of the CPSs. We consider the situation where the transmitted binary bits may be flipped over noisy communication channels. The primary purpose of this paper is to design a distributed resilient estimation scheme under a bit-flipped detection mechanism to achieve a reliable estimation of the CPSs. First, by means of the Minkowski sum of sets, a novel distributed zonotopic resilient estimation scheme is proposed where the optimal estimator gain is obtained in the minimum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F$</tex-math></inline-formula> -radius sense. Furthermore, a two-stage bit-flipped detection scheme is developed by taking advantage of 1) the prediction output zonotope and 2) the intersection between the prediction zonotope and the estimation zonotope. Furthermore, in order to improve the algorithm efficiency, a reduction operator is introduced to reduce the order of the zonotope. Finally, the IEEE 30-bus systems are applied to verify theoretical results of the developed distributed resilient estimation algorithm.

Edge Intelligent Joint Optimization for Lifetime and Latency in Large-Scale Cyber–Physical Systems

In recent years, the exploration on large-scale cyber–physical systems (CPSs) has become a fertile research field of significant impact. Large-scale CPS applications cover not only manufacturing and production areas but also daily living domains. Traditional solutions dedicated for large-scale CPSs mainly concentrate on the service latency or reliability optimization, but neglect the resultant negative impact on system lifetime. In this article, we conduct the first study on jointly optimizing the service latency and system lifetime subject to the constraints of reliability, energy consumption, and schedulability for large-scale CPSs. We propose an edge intelligent solution composed of offline and online phases. At the offline phase, the long short-term memory (LSTM) technique is leveraged to predict task offloading rates at individual user groups. Afterward, the multiobjective evolutionary algorithm with dual local search (DLS-MOEA) is exploited to determine optimal system static settings of computation offloading mapping and task replication number. At the online phase, an affinity-driven scheme incurring minimal system dynamic overheads is designed to deal with the inherent mobility of terminal users. We also build an algorithm validation platform upon which extensive simulation experiments are carried out. Experimental results show that our offline and online schemes outperform the state-of-the-art benchmarking methods by 27.1% and 43.5%, respectively.

Time-series clustering for sensor fault detection in large-scale Cyber–Physical Systems

Large-scale Cyber–Physical Systems (CPSs) are information systems that involve a vast network of sensor nodes and other devices that stream observations in real-time and typically are deployed in uncontrolled, broad geographical terrains. Sensor node failures are inevitable and unpredictable events in large-scale CPSs, which compromise the integrity of the sensors measurements and potentially reduce the quality of CPSs services and raise serious concerns related to CPSs safety, reliability, performance, and security. While many studies were conducted to tackle the challenge of sensor nodes failure detection using domain-specific solutions, this paper proposes a novel sensor nodes failure detection approach and empirically evaluates its validity using a real-world case study. This paper investigates time-series clustering techniques as a feasible solution to identify sensor nodes malfunctions by detecting long-segmental outliers in their observations’ time series. Three different time-series clustering techniques have been investigated using real-world observations collected from two various sensor node networks, one of which consists of 275 temperature sensors distributed around London. This study demonstrates that time-series clustering effectively detects sensor node’s continuous (halting/repeating) and incipient faults. It also showed that the feature-based time series clustering technique is a more efficient long-segmental outliers detection mechanism compared to shape-based time-series clustering techniques such as DTW and K-Shape, mainly when applied to shorter time-series windows.

Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-Physical Collectives

The engineering of large-scale cyber-physical systems (CPS) increasingly relies on principles from self-organisation and collective computing, enabling these systems to cooperate and adapt in dynamic environments. CPS engineering also often leverages digital twins that provide synchronised logical counterparts of physical entities. In contrast, sensor networks rely on the different but related concept of virtual device that provides an abstraction of a group of sensors. In this work, we study how such concepts can contribute to the engineering of self-organising CPSs. To that end, we analyse the concepts and devise modelling constructs, distinguishing between identity correspondence and execution relationships. Based on this analysis, we then contribute to the novel concept of “collective digital twin” (CDT) that captures the logical counterpart of a collection of physical devices. A CDT can also be “augmented” with purely virtual devices, which may be exploited to steer the self-organisation process of the CDT and its physical counterpart. We underpin the novel concept with experiments in the context of the pulverisation framework of aggregate computing, showing how augmented CDTs provide a holistic, modular, and cyber-physically integrated system view that can foster the engineering of self-organising CPSs.

Identifying and Analyzing Dependencies in and among Complex Cyber Physical Systems.

Contemporary Critical Infrastructures (CIs), such as the power grid, comprise cyber physical systems that are tightly coupled, to form a complex system of interconnected components with interacting dependencies. Modelling methodologies have been suggested as proper tools to provide better insight into the dependencies and behavioural characteristics of these complex systems. In order to facilitate the study of interconnections in and among critical infrastructures, and to provide a clear view of the interdependencies among their cyber and physical components, this paper proposes a novel method, based on a graphical model called Modified Dependency Structure Matrix (MDSM). The MDSM provides a compact perspective of both inter-dependency and intra-dependency between subsystems of one complex system or two distinct systems. Additionally, we propose four parameters that allow the quantitative assessment of the characteristics of dependencies, including multi-order dependencies in large scale CIs. We illustrate the workings of the proposed method by applying it to a micro-distribution network based on the G2ELAB 14-Bus model. The results provide valuable insight into the dependencies among the network components and substantiate the applicability of the proposed method for analyzing large scale cyber physical systems.

Social Collective Attack Model and Procedures for Large-Scale Cyber-Physical Systems.

A large-scale Cyber-Physical System (CPS) such as a smart grid usually provides service to a vast number of users as a public utility. Security is one of the most vital aspects in such critical infrastructures. The existing CPS security usually considers the attack from the information domain to the physical domain, such as injecting false data to damage sensing. Social Collective Attack on CPS (SCAC) is proposed as a new kind of attack that intrudes into the social domain and manipulates the collective behavior of social users to disrupt the physical subsystem. To provide a systematic description framework for such threats, we extend MITRE ATT&CK, the most used cyber adversary behavior modeling framework, to cover social, cyber, and physical domains. We discuss how the disinformation may be constructed and eventually leads to physical system malfunction through the social-cyber-physical interfaces, and we analyze how the adversaries launch disinformation attacks to better manipulate collective behavior. Finally, simulation analysis of SCAC in a smart grid is provided to demonstrate the possibility of such an attack.

An Improved Genetic Algorithm for Safety and Availability Checking in Cyber-Physical Systems

Cross-IoT infrastructure access frequently occurs when performing tasks in a distributed computing infrastructure of a cyber-physical system (CPS). The access control technology that ensure secure access cross-IoT infrastructure usually automatically establish relationships between user-attribute/role-permission. How to efficiently determine whether an automatic authorization access control state satisfies the safety and availability requirements of a system is a huge challenge. Existing work often focuses on a single aspect of safety or availability, while ignoring the differences between permissions and the differences between users. In this paper, we first propose a fine-grained personalization policy that takes into account the specificity of permissions/users and describes the safety, availability and efficiency requirements of an access control system in CPS. Second, we define a Personalization Policy Checking (PPC) Problem to determine whether a given personalization policy is satisfied in an access control state. We give the computational complexity of the PPC problem in different subcases, and show that it is NP-complete in general. Third, we design a binary genetic search algorithm, whose improvements mainly include continuous update and selection of the best chromosomes in the population for iteration, and exploring and determining the optimal crossover and mutation probabilities, thereby improving the convergence efficiency of the algorithm. Finally, simulation results show the effectiveness of our proposed algorithm, which is especially fit for the case that the computational overhead is even more important than the accuracy in a large-scale CPS system.

Control of Large-Scale Cyber-Physical Systems with Agents Having Various Dynamics

In this paper, a controller design methodology is developed for large-scale cyber-physical systems (CPSs) with agents having various dynamics. Especially, we propose a representative model of much lower order than the scale of the original CPS. This model facilitates designing controllers for large-scale CPSs. We derive necessary and sufficient conditions such that controllers designed for the proposed representative model stabilize the original CPS with given performance. The representative model is in the form of a nominal model with a variation of the agent models. This result shows that bad behavior of a few agents does not affect the stability or performance of the original system very much. Finally, the effectiveness of the proposed method is illustrated by simulation for a power grid.

BioCyBig: A Cyberphysical System for Integrative Microfluidics-Driven Analysis of Genomic Association Studies

This paper presents a research vision to design a large-scale cyberphysical systems (CPS) experimental framework to enable collaborative and coordinated molecular biology studies. This framework will be based on the integration of CPS with microfluidic biochips and cloud computing. It has the potential to drastically advance personalized medicine through knowledge fusion among many research groups, and synchronization of research planning. This framework therefore leads to a better understanding of diseases such as cancer, and helps researchers in identifying effective treatments. A case study from cancer research is discussed to explain the significance of our framework in promoting coordinated genomic studies.

An optimal graph-search method for secure state estimation

The growing complexity of modern Cyber–Physical Systems (CPS) and the frequent communication between their components make them vulnerable to malicious attacks. As a result, secure state estimation is a critical requirement for the control of these systems. Many existing secure state estimation methods suffer from combinatorial complexity which grows with the number of states and sensors in the system. This complexity can be mitigated using optimization-based methods that relax the original state estimation problem, although at the cost of optimality as these methods often identify attack-free sensors as attacked. In this paper, we propose a new optimal graph-search algorithm to correctly identify malicious attacks and to securely estimate the states even in large-scale CPS modeled as linear time-invariant systems The graph consists of layers, each one containing two nodes capturing a truth assignment of any given sensor, and directed edges connecting adjacent layers only. Then, our algorithm searches the layers of this graph incrementally, favoring directions at higher layers with more attack-free assignments, while actively managing a repository of nodes to be expanded at later iterations. The proposed search bias and the ability to revisit nodes in the repository and self-correct, allow our graph-search algorithm to reach the optimal assignment faster and tackle larger problems. We show that our algorithm is complete and optimal provided that process and measurement noises do not dominate the attack signal. Moreover, we provide numerical simulations that demonstrate the ability of our algorithm to correctly identify attacked sensors and securely reconstruct the state. Our simulations show that our method outperforms existing algorithms both in terms of optimality and execution time.

Semantic-based discovery method for high-performance computing resources in cyber-physical systems

High-performance computing (HPC) systems with powerful computing capabilities are becoming increasingly significant in the large-scale cyber-physical systems (CPS), helping CPS process a huge number of real-time data. Nowadays, the efficiency of HPC resource management and discovery becomes a challenging problem in CPS, due to the complex characteristics of HPC resources and the growing demands of the users. Although lots of recent efforts have been conducted to the resource discovery in distributed systems, they cannot be well adapted for the cross-regional HPC environments, due to the lack of unified model for the resources description and consideration for the usability demands of non-expert users. In this paper, we propose novel techniques to try to solve the problem. Specifically, we first propose a unified semantic model named HPCRO for specifying cross-regional HPC resources, and apply ontology reasoning to obtain more semantic information for queries. Moreover, we propose a WordNet-based quick resource index list data structure called WQRIL to improve the query. Finally, according to the proposed model and data structure, we propose an efficient discovery method called ROLD for cross-regional HPC resources. Extensive experimental results demonstrate that, our proposals not only maintain efficient resource discovery performance, but also achieve the highest precision rate (94.76%), recall rate (92.34%) and F1-score (93.53%).

Observer-based detection and identification of sensor attacks in networked CPSs

This paper presents several observer-based detection and identification strategies to characterize sensor attacks for descriptor type cyber–physical systems (CPSs). In contrast to recent works we do not consider state estimators relying on centralized paradigms, but propose distributed and decentralized state estimators to characterize sensor attacks. We also show a set of sufficient conditions to ensure exponential convergence of the estimation errors in the absence of malicious attacks, along with a bound on the convergence rates. We then design algorithms to solve the attack detection and identification problem for a large-scale CPS. Finally, a series of numerical experiments are performed on the IEEE-6 and 118 bus power systems.

A framework for automated multi-stage and multi-step product configuration of cyber-physical systems

Product line engineering (PLE) has been employed to large-scale cyber-physical systems (CPSs) to provide customization based on users’ needs. A PLE methodology can be characterized by its support for capturing and managing the abstractions as commonalities and variabilities and the automation of the configuration process for effective selection and customization of reusable artifacts. The automation of a configuration process heavily relies on the captured abstractions and formally specified constraints using a well-defined modeling methodology. Based on the results of our previous work and a thorough literature review, in this paper, we propose a conceptual framework to support multi-stage and multi-step automated product configuration of CPSs, including a comprehensive classification of constraints and a list of automated functionalities of a CPS configuration solution. Such a framework can serve as a guide for researchers and practitioners to evaluate an existing CPS PLE solution or devise a novel CPS PLE solution. To validate the framework, we conducted three real-world case studies. Results show that the framework fulfills all the requirements of the case studies in terms of capturing and managing variabilities and constraints. Results of the literature review indicate that the framework covers all the functionalities concerned by the literature, suggesting that the framework is complete for enabling the maximum automation of configuration in CPS PLE.

Grid Cyber-Physical System Risk Management Model Based on Cooperative Game Theory

AbstractAdvanced grid technology represented by smart grid and energy internet is the core feature of the next-generation power grid. The next-generation power grid will be a large-scale cyber-physical system (CPS), which will have a higher level of risk management due to its flexibility in sensing and control. This paper explains the methods and results of a study on grid CPS’s behavior after risk. Firstly, a behavior model based on hybrid automata is built to simulate grid CPS’s risk decisions. Then, a GCPS risk transfer model based on cooperative game theory is built. The model allows decisions to ignore complex network structures. On this basis, a modified applicant-proposing algorithm to achieve risk optimum is proposed. The risk management model proposed in this paper can provide references for power generation and transmission decision after risk as well as risk aversion, an empirical study in north China verifies its validity.

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.