[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119468-en":3,"doc-seo-119468-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119468,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Analysis of Machine-learning Algorithms for Detection of Cyber Attacks in Smart Grid - read online free","Smart grids support power-network digitalization through automation and data-driven decision-making, but increased dependence on these mechanisms introduces serious cybersecurity risks, particularly false data injection attacks (FDIA). FDIA can tamper with grid measurements, distorting generation or distribution decisions and potentially triggering large-scale outages. This thesis evaluates machine-learning algorithms for FDIA detection in power networks, comparing supervised and unsupervised approaches by accuracy, speed, and scalability. It also reviews mitigation techniques and how they can be integrated for real-time response and recovery, highlighting strengths, limitations, and future research directions.","ANALYSIS OF MACHINE-LEARNING ALGORITHMS FOR DETECTION OF CYBER ATTACKS IN SMART GRID  \nLappeenranta–Lahti University of Technology LUT  \nBachelor's Programme in Electrical Engineering, bachelor's thesis  \nBachelor's Programme in Electrical Engineering  \nIn co-operation with partner university: Hebei University of Technology  \n2025  \nJiacheng Zhu  \nExaminer(s): Researcher Hafiz Majid Hussain  \nProfessor Haiwen Chen  \nABSTRACT  \nLappeenranta–Lahti University of Technology LUT  \nLUT School of Energy Systems  \nElectrical Engineering  \nIn co-operation with partner university: Hebei University of Technology  \nJiacheng Zhu  \nAnalysis Of Machine-learning algorithms For Detection of Cyber Attacks In Smart Grid Bachelor’s thesis  \n2025 36 pages, 9 figures, 10 tables, and 1 appendix  \nExaminers: Researcher Hafiz Majid Hussain  \nKeywords: Smart Grids, Cybersecurity, False Data Injection Attacks, Machine Learning, Anomaly Detection, Mitigation Techniques  \nSmart grids are playing an essential role to digitalize power networks by enabling automation and data-driven methods. However, growing reliance on automation and datadriven decision-making makes these systems encounter various cyber threats, especially false data injection attacks (FDIA). These attacks can manipulate grid measurement data, leading to erroneous generation or distribution decisions and potentially causing largescale outages. This study researches the application of machine learning algorithms in power networks, with a particular focus on FDIA. It evaluates various detection technologies, which include supervised and unsupervised machine learning models, which include their accuracy, speed, and scalability. This study also examines existing mitigation strategies and their integration with machine learning models to enable real-time response and recovery. The results highlight the strengths and limitations of each detection approach, underscoring the importance of combining machine learning with strong cybersecurity measures to protect smart grids. Finally, this study discusses research opportunities, which are improving detection systems in the future and improving the smart grid's ability to respond to evolving cyber threats.  \nTable of contents  \nAbstract  \n1 Introduction......................................................................................................................... 6  \n2 Literature Study .................................................................................................................. 7  \n2.1 Cybersecurity Threats in Smart Grids .......................................................................... 7  \n2.2 Traditional Detection and Mitigation Strategies .......................................................... 8  \n2.3 Machine Learning in Cybersecurity and Smart Grid Applications..............................9  \n2.4 Previous Studies on ML-Based FDIA Detection ....................................................... 10  \n2.5 Challenges in Implementing ML in Real-Time Smart Grids..................................... 15  \n3 Methodology ..................................................................................................................... 16  \n3.1 Data Sources and Preprocessing ................................................................................ 16  \n3.1.1 Data Generation ................................................................................................... 17  \n3.1.2 Cyber-Attack Simulation ..................................................................................... 17  \n3.1.3 Feature Extraction ............................................................................................... 18  \n3.1.4 Preprocessing Steps ............................................................................................. 19  \n3.1.5 Data Partitioning.................................................................................................. 19  \n3.2 Supervised Learning Models for FDIA D","cbCaia4cqqKNhYyS","https://ap.wps.com/l/cbCaia4cqqKNhYyS","pdf",783001,1,64,"English","en",105,"# Abstract\n# Introduction\n# Literature Study\n## Cybersecurity Threats in Smart Grids\n## Traditional Detection and Mitigation Strategies\n## Machine Learning in Cybersecurity and Smart Grid Applications\n## Previous Studies on ML-Based FDIA Detection\n## Challenges in Implementing ML in Real-Time Smart Grids\n# Methodology\n## Data Sources and Preprocessing\n## Supervised Learning Models for FDIA Detection\n## Unsupervised Learning Models for FDIA Detection\n## Experimental Setup and Simulation Environment\n# Results\n## Comparative Analysis of ML Models\n## Strengths and Weaknesses of Detection Approaches\n## Case Study: Simulated FDIA Attack\n## Robustness Against Adversarial Attacks","[{\"question\":\"What problem does the thesis address in smart grids?\",\"answer\":\"It focuses on detecting false data injection attacks (FDIA) that can manipulate grid measurements and mislead operational decisions, potentially causing large-scale outages.\"},{\"question\":\"Which types of machine-learning models are evaluated for FDIA detection?\",\"answer\":\"The study evaluates both supervised and unsupervised machine-learning models, comparing them using criteria such as accuracy, speed, and scalability.\"},{\"question\":\"How does the thesis connect detection with mitigation?\",\"answer\":\"It examines existing mitigation strategies and discusses how they can be integrated with machine-learning models to support real-time response and recovery.\"}]","Analysis of Machine-learning Algorithms for Detection of Cyber Attacks in Smart Grid - 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