Detecting DDoS Attacks in the Internet of Medical Things Through Machine Learning Based Classification

The healthcare industry has witnessed a significant transformation due to the emergence of open-source medical cyber-physical systems, primarily driven by advancements in 3D printing technology. However, the growing use of these open-source systems in hospitals has also brought about cybersecurity concerns. In particular, adopting new technologies, such as mobile medical devices, has introduced new challenges when handling distributed denial of service (DDoS) attacks. Despite numerous statistical methods developed for DDoS attack detection, there remains a prominent concern regarding developing real-time detectors with low computational overhead. In addition, evaluating new detection algorithms and techniques heavily relies on the availability of well-designed datasets. Therefore, we create in this paper a new dataset called MedibotDDoS, including zero-day DDoS attacks, and perform a comparative study involving various machine learning algorithms based on a collection of network flow features utilizing the generated dataset to develop an effective strategy for detecting such DDoS attacks. The experimental evaluation results show that the Random Forest classifier performs the best and achieves the highest overall accuracy of 99.998%.