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

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.