Vol.13, No.4, November 2024.                                                                                                                                                                          ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

Web Application Firewall for Detecting and Mitigation of Based DDoS Attacks Using Machine Learning and Blockchain

Elva Leka, Luis Lamani, Admirim Aliti, Enkeleda Hoxha

© 2024 Elva Leka P, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 13, Issue 4, Pages 2802-2811, ISSN 2217-8309, DOI: 10.18421/TEM134-17, November 2024.

Received: 27 May 2024.

Revised: 16 September 2024.
Accepted: 21 October 2024.
Published: 27 November 2024.

Abstract:

Applications have rapidly transformed the data access, business operations, and communication methods. This sudden shift has resulted in significant security challenges such as Distributed Denial of Service (DDoS) attacks, which intensify Internet security issues. This paper introduces a novel approach to enhancing the Web Application Firewall (WAF) for detecting and mitigating botnet-based DDoS attacks through the use of Machine Learning (ML) and blockchain technologies. Legacy security systems often struggle to adapt to evolving digital threats, particularly with the rise of complex botnet designs. The integration of ML and blockchain within the WAF ecosystem represents a substantial advancement in cyber defense mechanisms. Insights are provided into the development of advanced ML algorithms for precise anomaly detection and the formulation of efficient blockchain protocols for streamlined threat intelligence sharing. The proposed approach addresses current challenges associated with botnet-driven DDoS attacks establishes a foundation for adaptive, future-proof cybersecurity strategies.

Keywords – Traffic analysis, DDoS attacks, cyber threats, web application security, real-time detection.

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