Deep Learning Approaches for DDoS Attack Detection in Communication Networks and IoT: A Comprehensive Review - Nabeel Fouad Abdulrahman; Mandeep Singh Jit Singh (Penerbit Universiti Kebangsaan Malaysia, 2025)
The increasing adoption of transformative technologies, such as the Internet of Things (IoT), has brought convenience and optimization to various domains. However, it has also introduced new challenges, including the vulnerability to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. DDoS attacks have shown an alarming rise in frequency and potency, making it crucial to devise effi cient mechanisms to prevent such attacks and safeguard communication networks. IoT networks, with their numerous interconnected devices and limited resources, are particularly susceptible to DDoS attacks. Traditional rule-based approaches have proven insuffi cient to cope with the dynamic nature of modern attacks, leading to the emergence of deep learning-based detection and mitigation techniques. Deep learning models, supported by real-world datasets, off er promising results with detection rates exceeding 98%. This study explores various deep learning architectures, focusing on their success in DDoS attack detection, particularly in IoT networks. It also addresses the challenges associated with such networks and highlights potential areas for future research.