A comprehensive review of combating EDoS attacks in cloud services with deep learning and advanced network security technologies including DDoS protection and intrusion prevention systems
1 Department of Fisheries and Aquaculture, J.S Tarkaa University, Makurdi, Nigeria.
2 Department of Computer Information Systems, Faculty of Computer Engineering, Prairie view A&M University, Prairie View, Texas, USA.
3 Department of Computing and Mathematical Sciences, University of Greenwich, Greenwich London, UK.
4 Department of Computer and Information Sciences, Northumbria University London, United Kingdom.
Review Article
Global Journal of Engineering and Technology Advances, 2024, 20(03), 006–033.
Article DOI: 10.30574/gjeta.2024.20.3.0168
Publication history:
Received on 29 July 2024; revised on 03 September 2024; accepted on 06 September 2024
Abstract:
This comprehensive review examines the current state of research and practice in combating Economic Denial of Sustainability (EDoS) attacks in cloud services, with a focus on deep learning approaches and advanced network security technologies. The paper provides an in-depth analysis of EDoS attack characteristics, their impact on cloud economics, and the challenges faced in mitigation efforts. It explores the application of deep learning techniques in EDoS detection and prevention, highlighting recent advancements in neural network architectures and feature extraction methods. The review also covers the integration of advanced network security technologies, including next-generation firewalls, software-defined networking, and cloud-native security solutions, in the context of EDoS protection. Furthermore, it discusses the adaptation of Distributed Denial of Service (DDoS) mitigation strategies for EDoS attacks, emphasizing traffic analysis and anomaly detection techniques. The role of Intrusion Prevention Systems (IPS) in EDoS mitigation is examined, comparing signature-based and behavior-based approaches and exploring their integration with other security components. The paper concludes by identifying emerging threats, regulatory considerations, and open research problems in EDoS protection, providing valuable insights for researchers and practitioners in the field of cloud security. This review aims to serve as a comprehensive resource for understanding the current landscape of EDoS attacks and defense mechanisms, while also highlighting future directions for research and development in this critical area of cloud computing security.
Keywords:
Economic Denial of Sustainability (EDoS); Cloud Security; Deep Learning, Intrusion Prevention Systems (IPS); DDoS Mitigation, Anomaly Detection; Machine Learning-enhanced Security
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0