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SCEN-SCADA Security: An Enhanced Osprey Optimization-Based Cyber Attack Detection Model in supervisory control and data acquisition system Using Serial Cascaded Ensemble Network

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OPTIMAL control APPLICATIONS & METHODS 2025年

作者： Alzubaidi, Fatimah Yaseen Hashim Kurnaz, Sefer Naseri, Raghda Awad Shaban Farhan, Hameed Mutlag Altinbas Univ Elect & Comp Engn Istanbul Turkiye Tikrit Univ Dent Dept Tikrit Iraq

A significant role of supervisory control and data acquisition (SCADA) systems is to support the operation of the energy system, where Information and Communication Technology (ICT) is utilized to interconnect devices, and this increases the system complexity. The interconnection of SCADA systems increases complexity and the potential for cybersecurity vulnerabilities. In addition, the SCADA networks with legacy devices are affected by inherent cybersecurity deliberation that has provided severe cybersecurity vulnerable points. With the adoption of local-area networks and Internet Protocol (IP)-driven proprietary, malicious or unauthorized user accesses the information from outside sources, and hence, the SCADA systems are weakened by the elaborate attacks. SCADA systems need to deliberate the Denial of Service (DoS) and catastrophic failure and maloperation, which may subsequently compromise the safety and stability of the operations in the power system. Therefore, the pertinent priority in SCADA is to strengthen cybersecurity to guarantee reliable operation, and also, the system stability is governed concerning communications integrity. The smart grid features are used in the conventional machine learning approaches for identifying cyber attacks. Hence, implementing an efficient and accurate cyber attack detection approach with less computational overhead is still a crucial research problem in SCADA. So, a novel and secure model for cyber attack detection in the SCADA system using advanced deep learning techniques together with the heuristic algorithm is executed in this research work. The SCADA data are collected from various power grids. The features from these data are optimally selected and fused with the optimal weights to obtain the weighted optimal features. The weighted optimal feature selection is done using the Enhanced Osprey Optimization Algorithm (EOOA). These optimally selected weighted features are given to the Serial Cascaded Ensemble Network (SCEN

关键词： cyber attack detection model enhanced osprey optimization algorithm serial cascaded ensemble network supervisory control and data acquisition system

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Improved Distribution Network Topology Generation with Hierarchical Information Overlay 18th

Improved Distribution Network Topology Generation with Hiera...

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18th International Conference on Critical Infrastructure Protection

作者： Gajic, Natasa Wolthusen, Stephen Norwegian Univ Sci & Technol Trondheim Norway Norwegian Univ Sci & Technol Gjovik Norway

ISBN: (纸本)9783031818905;9783031818882;9783031818875

Topological models of critical infrastructures such as power distribution networks (grids) enable the determination and validation of properties ranging from feasibility to susceptibility to attacks contingent on the placement of adversaries. Future distribution networks will require large numbers of sensors and actuators to be positioned efficiently. While it is possible to employ the same structures for power networks as information and control networks, this excessive constraint can reduce the robustness of power networks to attacks. This chapter extends previous work by incorporating a separate information layer that allows the incorporation of multiple hierarchical layers. The information layer facilitates the discovery of topological structures in which information and control aspects may still be retained, at least partially, in the presence of partial compromise of a power network structure by exploiting the limited redundancy in the information overlay hierarchy. The extended model also expresses the propagation of the effects of adversarial influence.

关键词： Topology Generation Distribution Network Communications and control Network Multilayered Graph supervisory control and data acquisition system

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Enhancing Grid Resilience: Islanding control in a Digital Twin of Real Electrical Distribution system 6

Enhancing Grid Resilience: Islanding Control in a Digital Tw...

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6th Global Power, Energy and Communication Conference (GPECOM)

作者： Sundhu, Anosh Arshad Saadatmandi, Soheil Arnone, Maurizio Links Fdn Turin Italy Politecn Torino Dipartimento Energia Galileo Ferraris Turin Italy

ISBN: (纸本)9798350351088;9798350351095

Digital Twin (DT) technology creates a digital counterpart of a physical system or process, facilitating instantaneous monitoring, analysis, and simulation. When applied to an Electrical Distribution system (EDS), DT enables continuous analysis by merging static and dynamic data, which illustrates the present condition of the grid and forecasts future scenarios for the Distribution system Operator (DSO), as well as for producers and consumers. It also provides the ability to simulate various hypothetical situations for DSOs. The current work is an extension of a previously published journal paper that discusses the architecture of the Smart Grid controller (SGC) application and the developed tools within it for Smart Recursive Load Flow (SRLF) calculation, aiming to estimate the loads in Medium Voltage (MV) feeders. The SGC application was designed to be integrated with the supervisory control and data acquisition system (SCADA) of any EDS to facilitate the development of DT. This paper focuses on the development of the Islanding control tool within the SGC application, presenting a control scheme for robust management and control of split grids. The islanding trigger entails specific instructions from the mainland system operator, deliberate disconnection maneuvers, and handling cascading failures in response to potential risks. This paper demonstrates the effectiveness of the developed Islanding control tool, which was evaluated on an actual EDS.

关键词： Digital Twin Distribution system Operator Electrical Distribution system Smart Grid controller supervisory control and data acquisition system Smart Recursive Load Flow

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Spatial-temporal attention and GRU based interpretable condition monitoring of offshore wind turbine gearboxes

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IET RENEWABLE POWER GENERATION 2022年第2期16卷 402-415页

作者： Su, Xiangjing Shan, Yanhao Li, Chaojie Mi, Yang Fu, Yang Dong, Zhaoyang Shanghai Univ Elect Power Coll Elect Engn Shanghai 200090 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia

Effective monitoring and early warning of gearbox operating status are of great significance to the operation and maintenance (O&M) of offshore wind turbines (WTs). This study proposes a normal behaviour modelling (NBM) method based on the spatial-temporal attention module and the gated recurrent unit (GRU), for the condition monitoring of offshore WT gearboxes. The proposed method has a superior performance by extracting the spatial and temporal features from the supervisory control and data acquisition (SCADA) system, and also has the unique advantage of model interpretability. Specifically, in the NBM training stage, the spatial features of offshore wind farm SCADA data are extracted by the spatial attention module firstly. Then, the temporal features of the spatial feature sequences above are extracted and fused by the GRU network. Afterwards, the temporal attention module is applied to strengthen the expression of key time points. In the NBM testing stage, the output residual between the predicted and the measured values is calculated and monitored by the exponential weighted moving average (EWMA) control chart. Finally, the effectiveness and superiority of the proposed NBM method are verified by detailed simulations on the Donghai Bridge offshore wind farm.

关键词： EWMA control chart spatial feature sequence extraction SCADA systems supervisory control and data acquisition system gated recurrent unit network power engineering computing normal behaviour modelling method temporal feature extraction GRU based interpretable condition monitoring control engineering computing gears offshore WT gearboxes control charts offshore wind turbine gearboxes recurrent neural nets offshore installations Donghai Bridge offshore wind farm offshore wind turbines condition monitoring feature extraction offshore wind farm SCADA data power system control wind turbines spatial feature extraction moving average processes exponential weighted moving average control chart GRU network spatial-temporal attention module sensor fusion NBM method spatial feature sequence fusion data acquisition gearbox operating status operation and maintenance wind power plants

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A GRU-Based Pseudo-Measurement Model for Dynamic State Estimation 2

A GRU-Based Pseudo-Measurement Model for Dynamic State Estim...

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2nd Asian Conference on Frontiers of Power and Energy (ACFPE)

作者： Xu, Zhibin Shao, Zhenguo Zhang, Yan Wu, Yangjian Fuzhou Univ Coll Elect Engn & Automat Fuzhou Peoples R China Fujian Prov Univ Fuzhou Univ Key Lab Energy Digitalizat Fuzhou Peoples R China

ISBN: (纸本)9798350303896

The challenge of unobservability arising from insufficient measurement equipment has emerged as a critical impediment to achieving precise state estimation in distribution networks. Integrating traditional supervisory control and data acquisition (SCADA) data with advanced metering infrastructure (AMI) data can effectively enhance the redundancy of network measurement information, resulting in a more extensive and accurate state estimation results. However, the low resolution of AMI data is incapable of supporting the high-resolution real-time state estimation. To address this issue, a dynamic state estimation method for distribution networks based on pseudo-measurement modeling is proposed. Firstly, the mapping model between SCADA data and AMI data is established based on Gated Recurrent Unit (GRU), enabling the generation of real-time high-resolution pseudo-measurement data for AMI. Secondly, leveraging the SCADA measurement timestamps, a multi-source measurement dataset incorporating both the pseudo-measurement data and real-time measurement data is constructed. And then the dynamic state estimation of the distribution networks can be achieved based on the dataset. Finally, simulation based on IEEE 33-bus test network verifies the effectiveness of the proposed method.

关键词： Pseudo-measurement modeling dynamic state estimation supervisory control and data acquisition system advanced metering infrastructure

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Sensors data Analysis in supervisory control and data acquisition (SCADA) systems to Foresee Failures with an Undetermined Origin

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SENSORS 2021年第8期21卷 2762页

作者： Javier Maseda, F. Lopez, Iker Martija, Itziar Alkorta, Patxi Garrido, Aitor J. Garrido, Izaskun Univ Basque Country UPV EHU Inst Res & Dev Proc Automat Control Grp ACGFac Engn Bilbao 48013 Spain RDT Co Intenance Munguia 48100 Spain Univ Basque Country UPV EHU Engn Sch Gipuzkoa Eibar 20600 Spain

This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives. The analysis of different data sources and the replacement of specific digital sensors with analog sensors improve the prognostic capacity for the detection of faults with an undetermined origin. Also presented is an anomaly detection algorithm to foresee failures and to recognize their occurrence even when they do not register as alarms or events. The improvement in machine availability after the implementation of the novel system guarantees the accomplishment of the proposed objectives.

关键词： industry 4 0 industrial internet of things supervisory control and data acquisition system machine learning

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Mining of intrusion attack in SCADA network using clustering and genetically seeded flora-based optimal classification algorithm

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IET INFORMATION SECURITY 2020年第1期14卷 1-11页

作者： Shitharth, Shitharth Shaik, Masood Ameerjohn, Sirajudeen Kannan, Sangeetha JNT Univ Vardhaman Coll Engn Dept CSE Hyderabad India JNT Univ Auroras Sci Technol & Res Acad Dept CSE Hyderabad India JNT Univ Auroras Sci Technol & Res Acad Dept ECE Hyderabad India Sri Satya Sai Univ Technol & Med Sci Dept CSE Pachama Madhya Pradesh India

The applications such as the remote communication and the control system are in critically integrated arrangement. The controlling of these network is specified by supervisory control and data acquisition (SCADA) systems. This study discusses about the attack prediction and classification process by using an enhanced model of machine learning technology. The attack types are classified by the optimal selection of features extracted from the sensor data. In this, the features are labelled and cluster between the matrixes are extracted. These cluster forms the initial processing of attack identification which prevents the mismatched result. This clustering of data is performed by mean-shift clustering algorithm. From that clustered data, the features that are irrelevant for classification process is identified and suppressed by using the genetically seeded flora optimisation algorithm. In this optimisation process, the flora seeds are selected genetically to select best features. Then, from that optimally selected clustered data, the relevancy vector is predicted and the types are classified. The classification process is performed by the Boltzmann machine learning algorithm. The classified results of the proposed method for testing SCADA dataset are analysed and the performance metrics are evaluated and compared with the state-of-the-art methods.

关键词： firewalls botany Boltzmann machines data mining pattern clustering learning (artificial intelligence) genetic algorithms SCADA systems pattern classification intrusion attack SCADA network genetically seeded flora-based optimal classification algorithm remote communication critically integrated arrangement attack prediction attack types optimal selection sensor data cluster forms attack identification mean-shift clustering algorithm genetically seeded flora optimisation algorithm flora seeds optimally selected clustered data Boltzmann machine learning algorithm SCADA dataset supervisory control and data acquisition system

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Early anomaly detection and localisation in distribution network: a data-driven approach

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第18期14卷 3814-3825页

作者： Shi, Xin Qiu, Robert He, Xing Ling, Zenan Yang, Haosen Chu, Lei Shanghai Jiao Tong Univ Dept Elect Engn 800 Dongchuan Rd Shanghai Peoples R China

The measurement data collected from the supervisory control and data acquisition (SCADA) system installed in distribution network can reflect the operational state of the network effectively. In this study, a random matrix theory-based approach is developed for early anomaly detection and localisation by using the data. For every feeder in the distribution network, a corresponding data matrix is formed. Based on the Marchenko-Pastur law for the empirical spectral analysis of covariance 'signal+noise' matrix, the linear eigenvalue statistics are introduced to indicate the anomaly, and the outliers and their corresponding eigenvectors are analysed for locating the anomaly. As for the low observability feeders in the distribution network, an increasing data dimension algorithm is designed for the formulated low-dimensional matrices being more accurately analysed. The developed approach can detect and localise the anomaly at an early stage, and it is robust to random disturbance and measurement error. Cases on Matpower simulation data and real SCADA data corroborate the feasibility of the approach.

关键词： eigenvalues and eigenfunctions spectral analysis SCADA systems random processes distribution network random matrix theory early anomaly detection data matrix data dimension algorithm Matpower simulation data SCADA data supervisory control and data acquisition system SCADA system Marchenko-Pastur law

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Improving the prediction of wind speed and power production of SCADA system with ensemble method and 10-fold cross-validation

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Case Studies in Chemical and Environmental Engineering 2023年 8卷

作者： Malakouti, Seyed Matin Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

This research used machine-learning-based forecasting models to estimate a supervisory control and data acquisition system's wind speed and electricity production. Six machine-learning algorithms were used to anticipate wind speed and electricity generation: a light gradient boosting machine, Gradient Boosting Regressor, an Ada Boost regressor, an elastic net, a lasso, and an ensemble (light gradient boosting machine and Ada Boost). To improve the performance of the light gradient boosting machine and Ada Boost algorithms, the ensemble (light gradient boosting machine and Ada Boost) was used to predict wind speed and production power of a supervisory control and data acquisition system. Besides the ensemble method, the results of machine learning algorithms were obtained using 10-fold cross-validation, 5-fold cross-validation, and 4-fold cross-validation methods. The results of the algorithms were compared with each other. The results showed that the root-mean-square error of the ensemble method (light gradient boosting machine and Ada Boost) in predicting the three-month production power of the supervisory control and data acquisition system was 11.78 with 10-fold cross-validation, and in predicting the wind speed was 0.2080 with 10-fold cross-validation. © 2023 The Author

关键词： 10-Fold cross-validation 4-fold cross-validation 5-Fold cross-validation Ensemble supervisory control and data acquisition system Wind speed

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Security Improvements by Separating the Cryptographic Protocol from the Network Stack onto a Multi-MCU Architecture 33rd

Security Improvements by Separating the Cryptographic Protoc...

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33rd International Conference on Architecture of Computing systems (ARCS)

作者： Frauenschlaeger, Tobias Renner, Sebastian Mottok, Juergen Tech Univ Appl Sci Regensburg Lab Safe & Secure Syst LaS3 D-93053 Regensburg Germany

ISBN: (纸本)9783030527938;9783030527945

The number of IoT devices in SCADA and ICS systems is rising quickly, especially in the domain of critical infrastructures. But these kinds of systems are performing mission critical tasks like controlling devices in industrial facilities or substations in the smart grid. Therefore, they are subject to a lot of regulatory standards. Yet, to provide remote access over the internet, special architectures are developed to integrate a network interface into these devices without inferring with the actual functionality. However, these architectures either lack security measures against cyber-attacks or do not offer the necessary performance for time-critical communication interfaces. To solve that, an architecture consisting of three units is introduced in this paper to provide a network interface with extensive security measures and a high performance. The main feature is the isolation of the cryptographic functionality onto an additional MCU. After proposing the basic concept, the paper presents many implementation details. Based on the current state of implementation, a concept validation of the realized architecture is described.

关键词： Cyber-security Functional safety Network security Industrial Internet of Things Industrial control system supervisory control and data acquisition system Multi microcontroller setup Dos prevention Critical infrastructures

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