Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks
Year: 2024
Type: review
Study design: Systematic Review
Abstract: The escalating integration of Internet of Things (IoT) devices has led to a surge in data generation within networks, consequently elevating the vulnerability to Distributed Denial of Service (DDoS) attacks. Detecting such attacks in IoT Networks is critical, and Machine Learning (ML) models have shown efficacy in this realm. This study conducts a systematic review of literature from 2018 to 2023, focusing on DDoS attack detection in IoT Networks using deep learning techniques. Employing the PRISMA methodology, the review identifies and evaluates studies, synthesizing key findings/2**. It highlights that incorporating deep learning significantly enhances DDoS attack detection precision and efficiency, achieving detection rates between 94 % and 99 %. Despite progress, challenges persist, such as limited training data and IoT device processing constraints with large data volumes. This review underscores the importance of addressing these challenges to improve DDoS attack detection in IoT Networks. The research's significance lies in IoT's growing importance and security concerns. It contributes by showcasing current state-of-the-art DDoS detection through deep learning while outlining persistent challenges. Recognizing deep learning's effectiveness sets the stage for refining IoT security protocols, and moreover, by identifying challenges, the research informs strategies to enhance IoT security, fostering a resilient framework.
Institution: Universidad de Salamanca
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: European Commission
OpenAlex ID: W4405993151