ItemBook Chapter

AI-driven defense mechanisms for Sybil and DDoS attacks in cloud networks

Title
AI-driven defense mechanisms for Sybil and DDoS attacks in cloud networks
Creator
Sharma, Arnav; Upreti, Kamal; Farooqui, Safia; Jain, Rituraj; Nijhawan, Pratham
Description
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.
Source
Cognitive Cloud Computing: Building Intelligent Systems for Tomorrow;pp.226-244
Date
01-01-2025
Publisher
Taylor and Francis
Coverage
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& A Consulting Global Pvt. Ltd, Gurgugram, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-104054278-1; 978-103294165-3;
Format
online
Language
English
Type
Book chapter

Citation

Sharma, Arnav; Upreti, Kamal; Farooqui, Safia; Jain, Rituraj; Nijhawan, Pratham, “AI-driven defense mechanisms for Sybil and DDoS attacks in cloud networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24399.

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