Improving Security in Internet of Medical Things through Hierarchical Cyberattacks Classification

The development of Internet of Medical Things (IoMT) devices has significantly enhanced healthcare quality but has also introduced critical cybersecurity vulnerabilities. While effective in some cases, traditional flat classification models often struggle to maintain accuracy when dealing with closely related IoMT cyber threats. This work addresses the issue by proposing a novel approach using hierarchical classification techniques for cyberattack detection and classification. Unlike traditional flat classification methods, our hierarchical approach considers the relationships between attack classes, organizing them into a structured hierarchy. We utilize the CICIoMT2024 dataset, the first specific dataset for IoMT cyberattacks, to develop and evaluate our model. Our results indicate that the hierarchical approach demonstrated high accuracy and proposed the hierarchical consistency of the data, compared to baseline flat classification approaches. The study concludes that hierarchical classification techniques offer a more nuanced method for detecting closely related attack types and hold significant potential for improving IoMT security, particularly in more complex hierarchical scenarios. These findings contribute valuable insights to the field of IoMT cybersecurity, suggesting that further research into more intricate hierarchical structures could yield even more effective security solutions.