Machine Learning-Based Security Algorithms for Detecting and Preventing DDoS Attacks on the IoT: State-of-the-Art, Challenges, and Future Directions - Baloyi, Coster; Mathonsi, Topside; Du Plessis, Deon; Muchenje, Tonderai; Tshilongamulenzhe, Tshimangadzo (The Indonesian Journal of Computer Science, 2025)
Abstract - The Internet of Things (IoT) represents a vast network of interconnected devices equipped with software, sensors, and other technologies that enable data exchange and autonomous operation with other devices and systems without human intervention over the internet. IoT applications span across various sectors, including agriculture, education, healthcare, and communication. However, Distributed Denial of Service (DDoS) attacks continue to pose significant risks to the IoT network due to current challenges of classification efficiency and response times by the existing algorithms, such as Decision Tree (DT), Linear Regression (LR), and K-means. This paper provides a comprehensive review of DDoS attack types within the IoT networks. Secondly, the paper critically examines and analyses the challenges and opportunities inherent in leveraging Machine Learning (ML) algorithms for detecting, preventing, and mitigating these attacks. Finally, it presents the categories of IoT performance metrics, and their statistics found in the Literature over the Past decade.