AI techniques for IoT-based DDoS attack detection: Taxonomies, comprehensive review and research challenges

Abstract

Distributed Denial of Service (DDoS) attacks in IoT networks are one of the most devastating and challenging cyber-attacks. The number of IoT users is growing exponentially due to the increase in IoT devices over the past years. Consequently, DDoS attack has become the most prominent attack as vulnerable IoT devices are becoming victims of it. In the literature, numerous techniques have been proposed to detect IoT-based DDoS attacks. However, techniques based on Artificial Intelligence (AI) have proven to be effective in the detection of cyber-attacks in comparison to other alternative techniques. This paper presents a systematic literature review of AI-based tools and techniques used for analysis, classification, and detection of the most threatening, prominent, and dreadful IoT-based DDoS attacks between the years 2019 to 2023. A comparative study of real datasets having IoT traffic features has also been illustrated. The findings of this systematic review provide useful insights into the existing research landscape for designing AI-based models to detect IoT-based DDoS attacks specifically. Additionally, the study sheds light on IoT botnet lifecycle, various botnet families, the taxonomy of IoT-based DDoS attacks, prominent tools used to launch DDoS attack, publicly available IoT datasets, the taxonomy of AI techniques, popular software available for ML/DL modeling, a list of numerous research challenges and future directions that may aid in the development of novel and reliable methods for identifying and categorizing IoT-based DDoS attacks.