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The approach combines Qualys API-based vulnerability scanning and reporting for identification, Docker deployment for security and isolation, advanced load balancing for resource optimisation, and machine-learning anomaly detection for threat identification and vulnerability prioritisation. The implementation generates a dataset containing details of simulated attacks used to train and evaluate a robust ML model. Experiments demonstrate high accuracy, precision, recall, and F1 across Denial of Service, Remote-toLocal, User-to-Root, and Probes.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/elevating-cybersecurity-for-smart-grid-systemsa-container-based-approach-enhanced-by-machine-learning-paper/290603/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/elevating-cybersecurity-for-smart-grid-systemsa-container-based-approach-enhanced-by-machine-learning-paper/290603.png","ImageObject",300,407,{"name":92,"@type":93},"Ezra","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What cybersecurity components does the proposed approach combine for smart grid containers?","Question",{"text":113,"@type":114},"It integrates Qualys API-based vulnerability scanning, Docker deployment for security isolation, load balancing for optimisation, and machine-learning anomaly detection for identifying threats and prioritising vulnerabilities.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is the dataset created and what attacks does it include?",{"text":118,"@type":114},"The system executes and logs four simulated attack types—Denial of Service, Remote-toLocal, User-to-Root, and Probes—on containerised nodes to form a realistic dataset for training and evaluation.",{"name":120,"@type":111,"acceptedAnswer":121},"How well does the machine learning model perform in the experiments?",{"text":122,"@type":114},"The experiments show exceptional performance across all conducted attacks, achieving high scores in accuracy, precision, recall, and F1.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290603,1789642175,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},1099514068035,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","2024 29th International Conference on Automation and Computing (ICAC) | 979-8-3503-6088-2/24/$31.00 ©2024 Crown | DOI: 10. 1 109/ICAC6 1394.2024. 10718762  \nElevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine  \nLearning  \n1st Mays Abukeshek  \nSchool of Computer Science University of Sunderland and University of Huddersfield, Sunderland and Huddersfield, United Kingdom 0009-0000-6340-0253  \n2nd Basel Barakat  \nSchool of Computer Science University of Sunderland Sunderland, United Kingdom 0000-0001-9126-7613  \n3rd Bamidele Ajayi  \nSchool of Computer Science University of Sunderland Sunderland, United Kingdom 0000-0003-1419-9375  \nAbstract— This paper presents a comprehensive implementation of a cybersecurity solution for smart grid network containers. The methodology utilises (i) Qualys APIbased vulnerability scanning and reporting system for vulnerability identification, (ii) Docker deployment for security and isolation, (iii) advanced load balancing techniques for resource optimisation, and (iv) machine learning-powered anomaly detection for threat identification and vulnerability prioritisation. The implementation was used to create a dataset that continues the details of several simulated attacks, enabling effective training and evaluation of a robust machine-learning model. The paper provides a thorough description of the implemented system architecture, the Qualys API-based vulnerability scanning and reporting system, the data set creation process, simulated attacks in Docker implementation, the load balancing process, and the machine learning model used for vulnerability prioritisation. The experiments showed that the machine learning model performed exceptionally well across all conducted attacks i.e., Denial of Service, Remote-toLocal, User-to-Root, and Probes, achieving high scores inaccuracy, precision, recall, and F1 scores.  \nKeywords— Cybersecurity, smart grid, ML, Attacks, API, Docker.  \nI. INTRODUCTION  \nIn 2023, the number of cybersecurity attacks witnessed a significant surge, highlighting the pressing need to safeguard critical infrastructure from these detrimental incidents. The study conducted by [1] Reveals that the frequency of cyberattacks escalated over the examined period. In 2018, there were 1,554 reported attacks, which increased to 1,667 in 2019, and further rose to 1,867 in 2020, reflecting a growth rate of approximately 17% between 2018 and 2020. Consequently, with the constant advances in cyber-attacks, it is imperative to establish robust mechanisms for detecting threats and safeguarding infrastructure. The field of cybersecurity in smart grid systems has been the subject of extensive research. The study by [2] presented a comprehensive examination of cybersecurity in smart grids highlighting prevalent security risks but overlooking container vulnerabilities. Similarly, [3] survey cybersecurity methods but lack a detailed analysis of container intricacies. On the other hand, [4] presents a smart grid security case study, which overlooks container vulnerabilities. The research conducted by [5] explored cybersecurity issues in smart grid communications, briefly mentioning container security. However, their investigation into container vulnerabilities was limited.  \nIn a similar vein, [7] conducted a review on security in communications with the smart grid and mentioned container vulnerabilities but did not provide a comprehensive analysis.  \nThe work in [8] proposed novel approaches and strategies, that emphasise the importance of adopting effective measures to address container vulnerabilities within smart grid systems, this emphasis draws from insights provided by studies on cyber security risk assessment, specifically focusing on container ports. By incorporating their innovative strategies into our research, which encompass network models for cyber-attacks evaluation, machine learning assisted, standards with cybersecurity requirements for smart grid, c","cbCaieqN4tdMGpfo","https://ap.wps.com/l/cbCaieqN4tdMGpfo","pdf",530128,"English","# Abstract\n# I. Introduction\n## Cybersecurity attack trends and the need for detection\n## Related work on smart grid security and container gaps\n## Dataset creation and attack coverage\n## Paper organisation","[{\"question\":\"What cybersecurity components does the proposed approach combine for smart grid containers?\",\"answer\":\"It integrates Qualys API-based vulnerability scanning, Docker deployment for security isolation, load balancing for optimisation, and machine-learning anomaly detection for identifying threats and prioritising vulnerabilities.\"},{\"question\":\"How is the dataset created and what attacks does it include?\",\"answer\":\"The system executes and logs four simulated attack types—Denial of Service, Remote-toLocal, User-to-Root, and Probes—on containerised nodes to form a realistic dataset for training and evaluation.\"},{\"question\":\"How well does the machine learning model perform in the experiments?\",\"answer\":\"The experiments show exceptional performance across all conducted attacks, achieving high scores in accuracy, precision, recall, and F1.\"}]","Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning - Paper | PDF",15]