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In today's technological era, the Internet has become ubiquitous, playing a vital role in our daily lives. With the exponential growth of IoT innovation, millions of interconnected IoT-enabled devices rely on cloud services to communicate over the Internet. However, this rapid development also exposes these devices to various threats, with DDoS (Distributed Denial of Service) and DoS (Denial of Service) attacks being particularly potent and destructive. DDoS attacks present a unique challenge as they are extremely difficult to detect using conventional intrusion detection frameworks and traditional methodologies. Fortunately, advancements in machine learning have provided a promising solution by enabling accurate differentiation between DDoS attacks and other forms of data. This study proposes a DDoS detection model based on machine learning algorithms. To conduct this study, we utilized the most recent and freely available online dataset called CICDDoS2019. Various machine learning-based techniques were explored, aiming to identify the characteristics associated with accurate classification. Among the algorithms tested, AdaBoost and XGBoost demonstrated exceptional performance. As part of future work, a hybrid approach will be incorporated into this model, further improving its capabilities. It is worth noting that this model will be continuously updated with new data on DDoS attacks, ensuring its relevance and effectiveness in combating emerging threats. By leveraging machine learning techniques, this approach enhances the detection of DDoS attacks on Internet of Things networks, safeguarding the integrity and security of connected devices and the overall IoT ecosystem.
Ashraf et al. (Wed,) studied this question.