Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sharma, Arnav; Upreti, Kamal; Farooqui, Safia; Jain, Rituraj; Nijhawan, Pratham Title A name given to the resource AI-driven defense mechanisms for Sybil and DDoS attacks in cloud networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Cognitive Cloud Computing: Building Intelligent Systems for Tomorrow;pp.226-244 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003569251-12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003569251-12</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105024631892?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105024631892?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sharma A., School of Sciences, CHRIST University, Delhi NCR, India; Upreti K., Department of Computer Science, Delhi NCR, Ghaziabad, India; Farooqui S., Centre for Online Learning, Dr. D. Y. Patil Vidyapeeth, Pune Sant Tukaram Nagar, Pimpri, Pune, India; Jain R., Department of Information Technology, Marwadi University, Rajkot, India; Nijhawan P., T&amp; A Consulting Global Pvt. Ltd, Gurgugram, India Description An account of the resource With massive DDoS attacks and Sybil attacks targeting national digital frameworks, financial institutions, and vital infrastructures, India is seeing an unparalleled increase in cyber threats. These attacks reveal significant vulnerabilities in national cybersecurity by jeopardizing system availability and integrity. Sybil attacks use numerous falsified identities to get unauthorized control over trust-based systems, while DDoS attacks flood networks with illegal traffic, making services inaccessible. This study investigates advanced machine learning (ML)-based identification and prevention strategies, including support vector machine (SVM), random forest (RF), decision tree (DT), and logistic regression (LR). To identify attack patterns, the methodology entails gathering actual network traffic data, preprocessing it to extract key information, and then using these models. To identify the best strategy, a comparison study is carried out depending on various parameters such as accuracy, precision, recall, and computing efficiency. The research suggests that random forest outperforms other ML algorithms in detecting Sybil attacks and DDoS attacks, achieving the maximum stability and accuracy. Nevertheless, the classification method is improved by merging decision trees and logistic regression, which further increases detection accuracy. In order to actively fight changing cyber threats, our findings highlight how important it is to include machine learning-driven security frameworks into India's cybersecurity infrastructure. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. Publisher An entity responsible for making the resource available Taylor and Francis Relation A related resource ISBN: 978-104054278-1; 978-103294165-3; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online