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Cybersecurity Requirements for Industrial Machine control systems

APPLIED SCIENCES-BASEL 2025年第3期15卷 1267-1267页

作者： Kasprzyczak, Leszek Manowska, Anna Dzwiarek, Marek Silesian Tech Univ Fac Min Safety Engn & Ind Automat PL-44100 Gliwice Poland Natl Res Inst Cent Inst Labour Protect PL-00701 Warsaw Poland

The first part of this paper discusses the research context, taking a closer look at the development of Industry 4.0 and the growing importance of the iioT, which entails new cybersecurity challenges. The issue of cyber threats and the need to increase the level of protection in machine control systems, which are particularly vulnerable to attacks due to their connection to the network, is also presented. The Introduction concludes with a presentation of the article's objective, which is to analyze the requirements of security levels (SLs) and the implementation of relevant international standards. The next section reviews the current research on cybersecurity in machine control systems. This section also points out the research gaps that the article aims to fill. The next section presents the risk assessment used to ensure safety during machine operations based on ISO 12100. The article describes safety functions implemented in machine control systems, including the SIL (safety integrity level) and PL (performance level) specifications. An important part of the article is the creation of a relationship between PL and SL, showing how the safety functions of systems are related to protection against cyber threats. The last part of the article gives a case study in the form of examples of machines and their control systems performing safety functions, which require various SLs depending on the PLs.

关键词： cybersecurity machinery control systems performance levels (PLs) security levels (SLs)

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Optimal Decision-making System of Shield Tunneling Parameters Based on LSTM and MNSGA-ii

Optimal Decision-making System of Shield Tunneling Parameter...

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IEEE International conference on Automatic control and Intelligent systems (I2CACIS)

作者： Wang, Shuai Zhang, Yakun Gong, Guofang Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China

ISBN: (纸本)9798350372113;9798350372106

This paper presents an optimal decision-making method for shield tunneling parameters that integrates time series prediction and multi-mode intelligent multi-objective optimization. Firstly, we have developed a decision-making scheme for determining optimal tunneling parameters. To achieve this, we utilize a Long Short-term Memory network (LSTM) which learns from the extensive experience of shield drivers. Then, A comprehensive excavation performance evaluation index system considering TBM excavation specific speed and excavation specific energy has been proposed. Next, the Pareto front surfaces of each surrounding rock grade are obtained through Multi-mode discrete NSGA-ii (MNSGA-ii), and the final solution is selected based on the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) between the Pareto solution set and the LSTM predicted value. To evaluate the effectiveness of the proposed scheme, it is deployed in a shield machine using edge computer, then the engineering experiments were conducted. The performance of the proposed decision-making system is then tested and compared with that of the shield driver. The test results show that the proposed decision-making system can significantly improve the comprehensive tunneling performance. This demonstrates the feasibility and effectiveness of the proposed decision-making system.

关键词： Shield tunnel boring machine Decision-making system Time series prediction Multi-objective optimization Swarm intelligence

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Artificial Neural network to Estimate Deterministic Indices in control Loop performance Monitoring 15th

Artificial Neural Network to Estimate Deterministic Indices ...

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15th International conference on Intelligent Human-Computer Interaction (IHCI)

作者： Gomez-Munera, John A. Diaz-Charris, Luis Jimenez-Cabas, Javier Corp Estudiantes & Profes Marinilla CORUM Marinilla Antioquia Colombia Univ Costa Barranquilla Atlantico Colombia

ISBN: (纸本)9783031538292;9783031538308

In many industrial processes, the control systems are the most critical components. Evaluate performance and robustness of a control loops is an important task to maintain the health of a control system and an efficiency in the process. In the area of control-Loop performance Monitoring (CPM), there are two groups of indices to evaluate the performance of the control loops: stochastic and deterministic. Using stochastic indices, a control engineer can calculate the performance indices of a control loop with the data in normal operation and a minimum knowledge of the process;but the problem is that to do a performance analysis is so hard, due it is necessary an advanced knowledge about the interpretation. Instead, an interpretation or analysis of deterministic indices is simpler;however, the problem with this approach is that an invasive monitoring of the plant is required to calculate the indices. In this paper, it is proposed to use an Artificial Neural network to estimate deterministic indices, considering as input the stochastic indices and some process information, taking advantage of the fact that data collection for stochastic indices is simpler.

关键词： Artificial Neural network control Loop performance Monitoring Deterministic Indices Stochastic Indices

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Goal-Oriented Transport Layer Protocols for Wireless control 20

Goal-Oriented Transport Layer Protocols for Wireless Control

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20th Annual IEEE International conference on Sensing, Communication, and networking (SECON)

作者： Kutsevol, Polina Ayan, Onur Pappas, Nikolaos Kellerer, Wolfgang Tech Univ Munich Chair Commun Networks Munich Germany Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden

ISBN: (纸本)9798350300529

Goal-oriented communication is a promising approach to tailor the network resource management algorithms to the needs of particular applications, thus enhancing the efficiency of resource utilization and boosting the application performance. In the context of distributed cyber-physical systems and networked control systems, the design of a control-aware transport layer (TL) represents a realistic approach for goal-oriented communications since it can be integrated into generic control setups without making assumptions on particular hardware or network technologies and deliver enhanced end-to-end performance. This demo showcases the application performance of different TL schemes used for communication between the sensors and the controllers monitoring and actuating inverted pendulums, i.e., multi-dimensional plants. The nodes of the control loops are realized with Zolertia Re-Mote devices, and multiple control loops communicate over the shared wireless network using IEEE 802.15.4 standard. We use the demonstration testbed to compare the performance of conventional, state-of-the-art, and novel goal-oriented TL schemes by observing the emulated dynamics of inverted pendulums.

关键词： Inverted pendulum

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Achieving Fairness Generalizability for Learning-based Congestion control with JURY 25

Achieving Fairness Generalizability for Learning-based Conge...

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20th European conference on Computer systems-EuroSys

作者： Tian, Han Liao, Xudong Sun, Decang Zeng, Chaoliang Jin, Yilun Zhang, Junxue Wan, Xinchen Wang, Zilong Wang, Yong Chen, Kai Univ Sci & Technol China Beijing Peoples R China Hong Kong Univ Sci & Technol iSING Lab Hong Kong Peoples R China BitIntelligence West Boylston MA USA

ISBN: (纸本)9798400711961

Internet congestion control (CC) has long posed a challenging control problem in networking systems, with recent approaches increasingly incorporating deep reinforcement learning (DRL) to enhance adaptability and performance. Despite promising, DRL-based CC schemes often suffer from poor fairness, particularly when applied to network environments unseen during training. This paper introduces Jury, a novel DRL-based CC scheme designed to achieve fairness generalizability. At its heart, Jury decouples the fairness control from the principal DRL model with two design elements: i) By transforming network signals, it provides a universal view of network environments among competing flows, and ii) It adopts a post-processing phase to dynamically module the sending rate based on flow bandwidth occupancy estimation, ensuring large flows behave more conservatively and smaller flows more aggressively, thus achieving a fair and balanced bandwidth allocation. We have fully implemented Jury, and extensive evaluations demonstrate its robust convergence properties and high performance across a broad spectrum of both emulated and real-world network conditions.

关键词： Congestion control Reinforcement Learning Transport Protocol

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Explainable Reinforcement Learning for network Management via Surrogate Model 44

Explainable Reinforcement Learning for Network Management vi...

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44th IEEE International conference on Distributed Computing systems (ICDCS)

作者： Botta, Alessio Canonico, Roberto Navarro, Annalisa Univ Napoli Federico II DIET Dept Naples Italy

ISBN: (纸本)9798350354720;9798350354713

In recent years, the adoption of Machine Learning, particularly Reinforcement Learning (RL), for the control and management of communication networks has emerged. However, a critical challenge hindering its practical implementation is the lack of explainability inherent in these models, which prevents network administrators from adopting these techniques despite their great potential to improve network performance. This paper aims to enhance the trustworthiness of RL-based network management and control, by making the RL model explainable and providing administrators with transparent insights into the RL decision-making processes. With this aim, we propose a methodology that leverages surrogate models, specifically, Decision Trees (DTs), to create simplified yet interpretable representations of the original RL model, able to explain it. Experiments were conducted to evaluate the efficacy of our method, demonstrating that the surrogate model achieves about 94% accuracy in imitating the original RL model. Additionally, the surrogate model significantly improves the explainability of the entire system by automatically generating graphical representations, in the form of DTs for interpreting the RL decisions.

关键词： Explainability network Management Reinforcement Learning SD-WAN

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Distribution of Roots of Quasi-Polynomials of Neutral Type and Its Application- Part ii: Consensus Protocol Design of Multi-Agent systems Using Delayed State Information

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IEEE TRANSACTIONS ON AUTOMATIC control 2024年第6期69卷 4058-4065页

作者： Wang, Honghai Han, Qing-Long Northeastern Univ Coll Informat Sci & Engn State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia

This article develops an approach to achieving consensus while improving the dynamic performance for a class of homogeneous multi-agent systems (MASs) using delayed state information via eigenvalue assignment. Note that the distribution of roots of quasi-polynomials plays a fundamental role in the consensus protocol design of the MASs. Some necessary conditions for the distribution of roots for a class of quasi-polynomials are first derived. Then, these conditions are applied to estimate the allowable regions of the protocol parameters. Next, some necessary and sufficient conditions for the determination of effective protocol parameters are established. An illustrative example is provided to show the effectiveness of the designed protocols.

关键词： Eigenvalues and eigenfunctions Consensus protocol Delays network topology Multi-agent systems Laplace equations Delay effects Consensus protocol design distribution of eigenvalues dynamic performance multi-agent systems (MASs) time delay

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A tri-objective model for cloudlet server placement problem in wireless metropolitan area networks

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SUSTAINABLE COMPUTING-INFORMATICS & systems 2025年 46卷

作者： Bahrami, Bahareh Khayyambashi, Mohammad Reza Univ Isfahan Fac Comp Engn Dept Comp Architecture Esfahan Iran Univ Isfahan Fac Comp Engn Dept Software Engn Esfahan Iran

To reduce latency and save energy, cloudlet computing enables tasks to be offloaded from user equipment to Cloudlet Servers (CSs). Determining the optimal number of CSs and the appropriate locations for their placement are two major challenges in building an efficient computing platform. Placing a CS at the closest location to the user can improve the QoS. Additionally, providing additional CSs to cover each user ensures that the user's needs are met even if the designated server is unable to provide services. However, to minimize energy consumption and costs, service providers tend to use a minimum number of CSs. Since the coverage zones of different CSs may overlap, fewer additional servers need to be deployed in such areas. This paper examines the problem of CS placement in a Wireless Metropolitan Area network (WMAN) and introduces a three-objective model that aims to optimize transmission distance, coverage with overlap control, and energy consumption. To obtain an appropriate Pareto front, the performance of the NSGA-ii, binary MOPSO, and binary MOGWO algorithms is examined through four different scenarios under the Shanghai Telecom dataset. Comparing the results of the HyperVolume (HV) indicator reveals that the NSGA-ii algorithm has higher values in all studied scenarios. A higher HV value means that the solution set is closer to an optimal Pareto set. In the best and worst case, the HV values for the NSGA-ii were equal to 0.2275 and 0.1883, respectively.

关键词： Cloudlet placement Wireless metropolitan area network (WMAN) NSGA-ii BMOGWO MOPSO

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Machine Learning Accelerated Real-Time Model Predictive control for Power systems

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 916-930页

作者： Ramij Raja Hossain Ratnesh Kumar Department of Electrical and Computer Engineering Iowa State UniversityAmesIA 50011 USA

This paper presents a machine-learning-based speedup strategy for real-time implementation of model-predictive-control(MPC)in emergency voltage stabilization of power *** success in various applications,real-time implementation of MPC in power systems has not been successful due to the online control computation time required for large-sized complex systems,and in power systems,the computation time exceeds the available decision time used in practice by a large *** long-standing problem is addressed here by developing a novel MPC-based framework that i)computes an optimal strategy for nominal loads in an offline setting and adapts it for real-time scenarios by successive online control corrections at each control instant utilizing the latest measurements,and ii)employs a machine-learning based approach for the prediction of voltage trajectory and its sensitivity to control inputs,thereby accelerating the overall control computation by multiple ***,a realistic control coordination scheme among static var compensators(SVC),load-shedding(LS),and load tap-changers(LTC)is presented that incorporates the practical delayed actions of the *** performance of the proposed scheme is validated for IEEE 9-bus and 39-bus systems,with±20%variations in nominal loading conditions together with *** show that our proposed methodology speeds up the online computation by 20-fold,bringing it down to a practically feasible value(fraction of a second),making the MPC real-time and feasible for power system control for the first time.

关键词： Machine learning model predictive control(MPC) neural network perturbation control voltage stabilization

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Optimal Output Tracking for Switched systems Under DoS Attacks: A Model-Free Adaptive Predictive control Method

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IEEE TRANSACTIONS ON CIRCUITS AND systems ii-EXPRESS BRIEFS 2024年第1期71卷 266-270页

作者： Qi, Yiwen Guo, Shitong Tang, Yiwen Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China Shenyang Aerosp Univ Sch Automat Shenyang 110136 Peoples R China

This brief studies a new resilient event-triggered model-free adaptive predictive control (MFAPC) method with anti-attacks for disturbed switched nonlinear systems in non-ideal network. The switched nonlinear systems are transformed into equivalent dynamic data models by dynamic linearization. Considering the denial of service (DoS) attacks in non-ideal network environment, an anti-attacks method based on a hold mechanism and a resilient event-triggering strategy (RETS) is considered, which reduces attacks impact on system performance. A parameter estimator is given to estimate the external disturbance and further obtain accurate system models. In addition, a new tracking error boundedness analysis method is given by using the average dwell time (ADT) technique and Lyapunov function. Finally, motor simulation results are given to verify the applicability of the proposed method.

关键词： Switches Nonlinear systems Predictive models Switched systems Integrated circuit modeling Data models Adaptation models Switched nonlinear systems MFAPC DoS attacks RETS tracking error

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