Industrial Cyber-physical Systems Research Articles

Solving industrial fault diagnosis problems with quantum computers

In this article, we investigate in how far quantum computers can be leveraged to solve NP-complete fault diagnosis problems within the area of industrial cyber-physical systems. Therefore, two approaches are proposed which exploit quantum computing to solve diagnosis problems: The first method employs Grover’s algorithm, and the second is based on the Quantum Approximate Optimization Algorithm. To show the industrial application, we present an integrated approach to learn the diagnosis model from process data, check whether the model is suitable, and use it for diagnosis. The result is a method for quantum industrial fault diagnosis. For this approach, the diagnostic capabilities and the runtime have been evaluated on an IBM Falcon processor using three publicly available benchmarks from the process industry. Further, the scaling between quantum computers and classical PCs has been analyzed.

Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts.

The Seventh IEEE International Conference on Industrial Cyber-Physical Systems [Society News

The Seventh IEEE International Conference on Industrial Cyber-Physical Systems [Society News

Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review

Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising solutions, and the digital transformation of industry towards green energy is slowly becoming a reality. New production planning rules, the optimization of the use of the Industrial Internet of Things (IIoT), industrial cyber-physical systems (ICPSs), and the effective use of production data and their optimization with AI bring further opportunities for sustainable, energy-efficient production. The aim of this study is to systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting. The value of the research includes the broader use of AI which will reduce the impact of the observed environmental and economic problems in the areas of reducing energy consumption, forecasting accuracy, and production efficiency. In addition, the demand for Green AI technologies in creating sustainable solutions, reducing the impact of AI on the environment, and improving the accuracy of forecasts, including in the area of optimization of electricity storage, will increase. A key emerging research trend in green energy management in manufacturing is the use of AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability. An innovative perspective that leverages AI’s ability to accurately forecast energy demand allows manufacturers to align energy consumption with production schedules, minimizing excess energy consumption and emissions. Advanced machine learning (ML) algorithms can integrate real-time data from various sources, such as weather patterns and market demand, to improve forecast accuracy. This supports both sustainability and economic efficiency. In addition, AI-based demand forecasting can enable more dynamic and responsive energy management systems, paving the way for smarter, more resilient manufacturing processes. The paper’s contribution goes beyond mere description, making analyses, comparisons, and generalizations based on the leading current literature, logical conclusions from the state-of-the-art, and the authors’ knowledge and experience in renewable energy, AI, and mechatronics.

Two channels event‐triggered robust H‐infinity control of Luire industrial cyber physical systems under data injection attacks

AbstractBy fully taking the effects of data injection attacks, nonlinear disturbances and limited communication resources, the stability of Lurie industrial cyber physical systems with the time‐varying delays is investigated. The two channels event‐triggered dynamic robust control (ERHIC) strategy is proposed for achieving the stability of systems. On one hand, a dynamic output feedback controller is designed to ensure the stable operation of the system under attacks. On other hand, the measurement channel and the control channel (two channels) event‐triggered mechanisms are designed to improve the utilization rate of system resources. The simulation results with the size of capacitor voltage and inductor current under data injection attacks as the object verity the correctness and the effectiveness of the proposed two channels event‐triggered robust H‐infinity control method and the feasibility of the designed dynamic output feedback controller are verified by the simulation results.

Enhanced collaborative intrusion detection for industrial cyber-physical systems using permissioned blockchain and decentralized federated learning networks

In the digital economy, the security of Cyber-Physical Systems (CPSs) is paramount. Despite the deployment of various Intrusion Detection Systems (IDSs) in industrial CPSs, the primary obstacles to the advanced research include class imbalance, privacy gap, model homogenization, and arbitrary aggregation. Thus, in this paper, a Permissioned Blockchain-enabled decentralized federated distillation Generative Adversarial Network (GAN), namely PB-fdGAN, is proposed for Collaborative IDSs (CIDSs) in industrial CPSs. The novel Local Differential Privacy (LDP)-external classifier GAN (ecGAN) addresses class imbalance and privacy concerns by integrating label conditions into the latent space and using Wasserstein distance for stability in semi-supervised learning. Additionally, a Decentralized Federated Distillation (DFD) scheme allows for collaborative model building without data exchange, enhancing privacy and diversity. Moreover, a Quality of Service (QoS)-consortium blockchain framework with a new QoS evaluation strategy ensures reliable and effective model aggregation. The experimental evaluation demonstrates the high effectiveness of PB-fdGAN in detecting various types of cyber threats and the superiorities over state-of-the-art IDS solutions.

Generative AI and Cognitive Computing-Driven Intrusion Detection System in Industrial CPS

Industrial Cyber-Physical Systems (ICPSs) are becoming more and more networked and essential to modern infrastructure. This has led to an increase in the complexity of their dynamics and the challenges of protecting them from advanced cyber threats have escalated. Conventional intrusion detection systems (IDS) often struggle to interpret high-dimensional, sequential data efficiently and extract meaningful features. They are characterized by low accuracy and a high rate of false positives. In this article, we adopt the computational design science approach to design an IDS for ICPS, driven by Generative AI and cognitive computing. Initially, we designed a Long Short-Term Memory-based Sparse Variational Autoencoder (LSTM-SVAE) technique to extract relevant features from complex data patterns efficiently. Following this, a Bidirectional Recurrent Neural Network with Hierarchical Attention (BiRNN-HAID) is constructed. This stage focuses on proficiently identifying potential intrusions by processing data with enhanced focus and memory capabilities. Next, a Cognitive Enhancement for Contextual Intrusion Awareness (CE-CIA) is designed to refine the initial predictions by applying cognitive principles. This enhances the system’s reliability by effectively balancing sensitivity and specificity, thereby reducing false positives. The final stage, Interpretive Assurance through Activation Insights in Detection Models (IAA-IDM), involves the visualizations of mean activations of LSTM and GRU layers for providing in-depth insights into the decision-making process for cybersecurity analysts. Our framework undergoes rigorous testing on two publicly accessible industrial datasets, ToN-IoT and Edge-IIoTset, demonstrating its superiority over both baseline methods and recent state-of-the-art approaches.

Anomaly Detection Based on Data Super-Resolution in Industrial Cyber–Physical Systems With Multirate Sampling

Anomaly Detection Based on Data Super-Resolution in Industrial Cyber–Physical Systems With Multirate Sampling

IDG-SemiAD: An Immune Detector Generation-Based Collaborative Learning Scheme for Semi-supervised Anomaly Detection in Industrial Cyber-physical Systems

Anomaly detection is a critical line of defense to ensure the network security of industrial cyber-physical systems. However, a significant issue in the anomaly detection is the insufficient labels of anomaly classes. With emergence of the new and unknown network attacks, accurately labeling these attacks can be a costly task. The issue of inadequate labeling may negatively impact the detection performance of many existing anomaly detection methods. To meet this gap, this paper proposes a semi-supervised collaborative learning paradigm called IDG-SemiAD, based on an immune detector generation algorithm. First, we design an immune detector generation algorithm based on a chaos map to generate abnormal samples from self-samples. Then, these abnormal samples are combined with self-samples and given specific labels to form a new training set. Finally, the LightGBM classifier is used for training and detection. Experiments on the widely used public dataset BATADAL show that the proposed IDG-SemiAD outperforms the classical v-detector method in terms of recall and f-score, with improvements of 8.2% and 8%, respectively, and outperforms deep learning-based anomaly detection methods, with a maximum improvements of up to 89.7% and 59.5% respectively.

TICPS: A trustworthy collaborative intrusion detection framework for industrial cyber–physical systems

The networking of industrial cyber–physical systems (CPS) introduces increased security vulnerabilities, necessitating advanced intrusion detection systems (IDS). Many current studies aiming to enhance IDS capabilities leverage Federated Learning (FL) technology for collaborative intrusion detection. However, devices deployed in an industrial setting in a distributed manner are vulnerable to cyber and poisoning attacks. Compromised clients can create malicious parameters to disrupt intrusion detection models, making them ineffective in identifying attacks. Nevertheless, existing FL-based intrusion detection methods exhibit suboptimal performance in detecting malicious clients and resisting poisoning attacks. To address these issues, we propose TICPS, a collaborative intrusion detection framework based on a trustworthy model update strategy to detect cyber threats from industrial CPS. The framework enables multiple industrial CPS to collaboratively construct an intrusion detection model and evaluate the security of each industrial CPS node using an update evaluation mechanism, ensuring effective intrusion detection even in the presence of poisoning. Extensive experiments on real-world industrial CPS datasets demonstrate that TICPS can effectively detect various types of cyber threats targeting industrial CPS. In particular, the framework achieves an intrusion detection accuracy of 94% even when the proportion of malicious agents reaches 80% under three typical poisoning attacks.

Contradictions Identification of Safety and Security Requirements for Industrial Cyber–Physical Systems

Contradictions Identification of Safety and Security Requirements for Industrial Cyber–Physical Systems

Endpoint cybersecurity: When smart devices turn stupid

With the proliferation of ever-cheaper IoTs (or smart devices) from smart doorbells to WiFi security cameras and baby monitors and hybrid work arrangements involving relaxed BYOD (Bring Your Own Device) policies, cybersecurity education must build awareness on the threats that endpoints pose. While securing corporate networks from cyberattack gains much attention, there is a dearth of educational material that exemplifies the processes by which endpoints are used in hacks, and how to build defenses against endpoint vulnerabilities. This teaching case provides a Defense in Depth (DiD) learning framework on how to defend industrial cyber-physical systems, digital infrastructure and assets. The case illustrates the applicability of a DiD framework independent of attack vector yet particular to endpoint vulnerabilities and shows how lessons from the past can help defend and mitigate future cyberattacks.

A Systematic Analysis of Security Metrics for Industrial Cyber–Physical Systems

Nowadays, as the cyber-threat landscape is evolving and digital assets are proliferating and becoming more and more interconnected with the internet and heterogeneous devices, it is fundamental to be able to obtain a sensible measure of the security of devices, networks, and systems. Industrial cyber–physical systems (ICPSs), in particular, can be exposed to high operational risks that entail damage to revenues, assets, and even people. A way to overcome the open question of measuring security is with the use of security metrics. With metrics it is possible to rely on proven indicators that benchmark systems, identify vulnerabilities, and show practical data to assess the risk. However, security metrics are often proposed with specific contexts in mind, and a set of them specifically crafted for ICPSs is not explicitly available in the literature. For this reason, in this work, we analyze the current state of the art in the selection of security metrics and we propose a systematic methodology to gather, filter, and validate security metrics. Then, we apply the procedure to the ICPS domain, gathering almost 300 metrics from the literature, analyzing the domain to identify the properties useful to filter the metrics, and applying a validation framework to assess the validity of the filtered metrics, obtaining a final set capable of measuring the security of ICPSs from different perspectives.

CyberDefender: an integrated intelligent defense framework for digital-twin-based industrial cyber-physical systems

CyberDefender: an integrated intelligent defense framework for digital-twin-based industrial cyber-physical systems

A Digital Twin Development Framework for an Electrical Submersible Pump (ESP)

Premature failure of a sub-system can be critical for an industrial Cyber-Physical System (CPS). A digital twin (DT) assisted predictive maintenance procedure can reduce the risk of costly unplanned maintenance. This study presents a generalized DT development framework for an Electrical Submersible Pump (ESP) that can assist in predictive maintenance. The framework is applied on a single-phase ESP as a proof of concept. The maximum winding temperature of the selected ESP is simulated using a multiphysics simulation tool with transient electromagnetic and transient heat transfer solvers. The simulation parameters were refined using data captured through an ESP free-run experiment. Simulating the total energy loss in the ESP stator and rotor and the transfer of heat from the outer fluid domain facilitates a relationship between the measurable external temperature and the maximum temperature in the stator winding. Following a design of experiments (DOE) approach, a series of simulations were run to establish a statistical model for the winding temperature in terms of the fluid temperature, the time duration a particular temperature was persistent, and the initial maximum stator winding temperature. As the instantaneous maximum stator winding temperature is related to the remaining useful lifetime, it was shown using a case study that the proposed framework can prognosticate the ESP failure, assisting effective decision-making for predictive maintenance of a CPS.

Lightweight privacy-preserving authentication mechanism in 5G-enabled industrial cyber physical systems

With the deep integration of informatization and industrialization, cyber-physical system (CPS), which integrates computing, communication and control technologies, comes into being and has been widely used in industrial applications. A large number of information physical system devices and control systems are based on open internet connections, and security and privacy protection issues are gradually emerging, especially in the process of dynamic and difficult to control information physical system stream data transmission and storage, which will face new security threats. To alleviate these issues, user authentication mechanisms can prevent unauthorized access by adversaries in the CPS environment. For this purpose, we put forth a lightweight privacy-preserving authentication scheme for 5G-enabled ecosystems. This method utilizes edge devices to save online transaction data in network and securely transmits IoT devices data from the sensors to the edge gateway. Then, the edge gateway delivers and stores these data in the cloud. A detailed comparative study based on experimental results indicates that our proposal achieves a better balance between security and functionality features, communication and capitulation costs compared to other existing competitive solutions.

The Impact of Network Design Interventions on the Security of Interdependent Systems

We study the problem of defending a Cyber-Physical System (CPS) consisting of interdependent components with heterogeneous sensitivity to investments. In addition to the optimal allocation of limited security resources, we analyze the impact of an orthogonal set of defense strategies in the form of network design interventions in the CPS to protect it against the attacker. We first propose an algorithm to simplify the CPS attack graph to an equivalent form which reduces the computational requirements for characterizing the defender's optimal security investments. We then evaluate four types of design interventions in the network in the form of adding nodes in the attack graph, interpreted as introducing additional safeguards, introducing structural redundancies, introducing functional redundancies, and introducing new functionalities. We identify scenarios in which interventions that strengthen internal components of the CPS may be more beneficial than traditional approaches such as perimeter defense. We showcase our proposed approach in two practical use cases: a remote attack on an industrial CPS and a remote attack on an automotive system. We highlight how our results closely match recommendations made by security organizations and discuss the implications of our findings for CPS design.

AGRIC: Artificial-Intelligence-Based Green Routing for Industrial Cyber–Physical System Pertaining to Extreme Environment

AGRIC: Artificial-Intelligence-Based Green Routing for Industrial Cyber–Physical System Pertaining to Extreme Environment

Bayesian and stochastic game joint approach for Cross-Layer optimal defensive Decision-Making in industrial Cyber-Physical systems

The propagation of cyber-attacks targeting modern industrial cyber-physical systems (ICPSs) is considered a sophisticated and persistent cross-layer penetration process, posing significant cyber-to-physical (C2P) risks to critical industrial infrastructures. However, existing defensive solutions for ICPSs lack the global defensive decision-making capacity against such persistent and stealthy cross-layer threats. This paper proposes a Bayesian-Stochastic hybrid game-theoretic approach that can generate optimal defensive strategies throughout the cross-layer penetration lifecycle, even without full information about attackers. Specifically, we first present a unified quantification framework to guarantee consistent utility function configurations in a cyber-physical layer integrated system. Then, we propose a state transition-based stochastic game model to characterize the dynamic evolution process of cross-layer penetration. The attack-defense interaction in each state is modeled as an incomplete-information Bayesian game to capture the inherent uncertainties of attacker information. Furthermore, a multi-agent Bayesian Q-learning (MABQL) algorithm is developed to learn the optimal defensive strategies despite incomplete information. The proposed approach is implemented and assessed based on a real-world ICPS testbed and the numerical results validate its feasibility and effectiveness for making optimal defensive decisions.

Detect Insider Attacks in Industrial Cyber-physical Systems Using Multi-physical Features-based Fingerprinting

ICPS software and hardware suffer from low update frequency, making it easier for insiders to bypass external defenses and launch concealed destructive attacks. To address these concerns, we design a device fingerprinting method based on multi-physical features, augmenting current intrusion detection techniques in the ICPS environment. In this article, we use the sorting system as an example, demonstrating that the proposed device fingerprinting technology has generality in the intrusion detection of ICPS control flow. Specifically, we first formalize the physical model of the sorting system to analyze the critical device features. Then, we extract these physical features from the sensor data collected in a physical testbed. Utilizing featurized data, we train a classifier that generates fingerprints in real-time in the production environment. Moreover, we develop a differential detection model based on device fingerprints to discover stealthy insider attacks efficiently. We evaluate the proposed method in a real-world testbed. Experiment results show that the detecting performance of classifiers approaches 100% when the the number of component types is small.