[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117060-en":3,"doc-seo-117060-105":30,"detail-sidebar-cat-0-en-105":92},{"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},117060,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Safeguarding Critical Infrastructures - Machine Learning in Cybersecurity","Protecting critical infrastructures from cyber threats is essential as technology permeates daily life and underpins public safety and national security. The article investigates how machine learning and cybersecurity work together to strengthen defenses for vital systems such as power grids, transportation networks, and healthcare. It highlights limitations of traditional signature-based approaches against fast-evolving attacks and emphasizes anomaly detection using CNN, LSTM, and deep reinforcement learning, while addressing data privacy, algorithm transparency, and adversarial threats that must be mitigated for successful deployment.","Safeguarding Critical Infrastructures: Machine Learning in Cybersecurity  \n1Dr. Aarti Kalnawat, 2*Dharmesh Dhabliya 3Kasichainula Vydehi 4 Anishkumar Dhablia, 5 Prof. Santosh D. Kumar  \n1 Assistant Professor, Symbiosis Law School, Nagpur Campus, Symbiosis International (Deemed University), Pune, India. Email: [deeptik@slsnagpur.edu.in](deeptik@slsnagpur.edu.in)  \n2Professor, Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India Email: [dharmesh.dhabliya@viit.ac.in](dharmesh.dhabliya@viit.ac.in)  \n3Associate Professor, Dept of CSE, Aditya Engineering College, Surampalem, India  \n4 Engineering Manager, Altimetrik India Pvt Ltd, Pune, Maharashtra, India Email: [anishdhablia@gmail.com](anishdhablia@gmail.com)  \n5Department of Artificial Intelligence & Data Science, Vishwakarma Institute of Information Technology, Pune, INDIA. Email: [santosh.kumar@viit.ac.in](santosh.kumar@viit.ac.in)  \nABSTRACT:It has become essential to protect vital infrastructures from cyber threats in an age where technology permeates every aspect of our lives. This article examines how machine learning and cybersecurity interact, providing a thorough overview of how this dynamic synergy might strengthen the defence of critical systems and services. The hazards to public safety and national security from cyberattacks on vital infrastructures including electricity grids, transportation networks, and healthcare systems are significant. Traditional security methods have failed to keep up with the increasingly sophisticated cyber threats. Machine learning offers a game-changing answer because of its ability to analyse big datasets and spot anomalies in real time. The goal of this study is to strengthen the defences of key infrastructures by applying machine learning algorithms, such as CNN, LSTM, and deep reinforcement learning for anomaly algorithm. These algorithms can anticipate weaknesses and reduce possible breaches by using historical data and continuously adapting to new threats. The research also looks at issues with data privacy, algorithm transparency, and adversarial threats that arise when applying machine learning to cybersecurity. For machine learning technologies to be deployed successfully, these obstacles must be removed.  \nProtecting vital infrastructures is essential as we approach a day where connectivity is pervasive. This study provides a road map for utilising machine learning to safeguard the foundation of our contemporary society and make sure that our vital infrastructures are robust in the face of changing cyberthreats. The secret to a safer and more secure future is the  \nmarriage of cutting-edge technology with cybersecurity knowledge.  \nKeywords: Machine Learning, Cybersecurity, Critical Infrastructures,  \nCNN, LSTM  \n* Corresponding author Email: [dharmesh.dhabliya@viit.ac.in](dharmesh.dhabliya@viit.ac.in)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \n1. INTRODUCTION  \nThe security of critical infrastructures has grown to be a major worldwide problem in our increasingly linked world, where information technology is at the heart of almost every element of contemporary civilization. Power grids, transportation networks, healthcare facilities, and financial institutions are just a few of the many systems and services that make up these infrastructures, all of which are essential to the operation of states and the welfare of their populations [1] . However, the very same technologies that increase our productivity and convenience also put these infrastructures at risk from a wide range of evolving cyber threats. In this context, the fusion of cybersecurity and machine learning offers enormous potential for bolstering our defences against these dangers and ushering ina new era of s","cbCaib2LkWhg0iq9","https://ap.wps.com/l/cbCaib2LkWhg0iq9","pdf",2072207,1,16,"English","en",105,"# Introduction\n## Motivation and significance\n## Limitations of traditional security\n## Machine learning paradigm shift\n## Scope and objectives","[{\"question\":\"Why is safeguarding critical infrastructures a major cybersecurity challenge?\",\"answer\":\"Critical infrastructures such as power grids, transportation networks, and healthcare are essential to state operations and public welfare. Their exposure to evolving cyber threats creates financial, operational, public-safety, and national-security risks.\"},{\"question\":\"What limitations do traditional security methods have against modern attacks?\",\"answer\":\"Signature-based firewalls and intrusion detection systems struggle with high attack volumes and rapidly changing hostile techniques. Attackers also use polymorphic malware, zero-day vulnerabilities, and social engineering to evade detection.\"},{\"question\":\"How can machine learning improve cyber defense for critical systems?\",\"answer\":\"Machine learning can analyze large datasets, detect anomalies in real time, and provide early warnings by learning from historical behavior. The study mentions using CNN, LSTM, and deep reinforcement learning for anomaly detection while continuously adapting to new threats.\"}]","Safeguarding Critical Infrastructures - Machine Learning in Cybersecurity | PDF",1785673493,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"safeguarding-critical-infrastructures-machine-learning-in-cybersecurity","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/safeguarding-critical-infrastructures-machine-learning-in-cybersecurity/117060/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is safeguarding critical infrastructures a major cybersecurity challenge?","Question",{"text":76,"@type":77},"Critical infrastructures such as power grids, transportation networks, and healthcare are essential to state operations and public welfare. Their exposure to evolving cyber threats creates financial, operational, public-safety, and national-security risks.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What limitations do traditional security methods have against modern attacks?",{"text":81,"@type":77},"Signature-based firewalls and intrusion detection systems struggle with high attack volumes and rapidly changing hostile techniques. Attackers also use polymorphic malware, zero-day vulnerabilities, and social engineering to evade detection.",{"name":83,"@type":74,"acceptedAnswer":84},"How can machine learning improve cyber defense for critical systems?",{"text":85,"@type":77},"Machine learning can analyze large datasets, detect anomalies in real time, and provide early warnings by learning from historical behavior. 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