Ensemble and Deep Neural Network Methods for Securing IoT Networks Against DDoS Attacks: A Comprehensive Literature Review
TL;DR
This paper presents a comprehensive literature review of DDoS detection and mitigation techniques in IoT environments, emphasizing traditional methods, machine learning, deep learning, and ensemble-based approaches.
Abstract
The rapid expansion of the Internet of Things (IoT) has enabled pervasive connectivity and intelligent automation across multiple sectors, while simultaneously exposing networks to significant security vulnerabilities. Among these vulnerabilities, Distributed Denial-of-Service (DDoS) attacks remain one of the most critical threats due to their scalability, persistence, and effectiveness against resource‑constrained IoT devices. This paper presents a comprehensive literature review of DDoS detection and mitigation techniques in IoT environments, emphasizing traditional methods, machine learning, deep learning, and ensemble-based approaches. The review is structured into four major sections: the IoT and DDoS threat landscape; traditional and machine learning-based detection techniques; deep learning and ensemble approaches; and open challenges with future research directions. By synthesizing existing studies, this paper provides a coherent academic foundation for the design of adaptive, scalable, and intelligent IoT security frameworks.