ECU-IoHT: A dataset for analyzing cyberattacks in Internet of health things
ORCID : 0000-0002-4559-4768
ORCID : 0000-0003-3368-2215
ORCID : 0000-0003-1365-0929
Ad Hoc Networks
School of Science
In recent times, cyberattacks on the Internet of Health Things (IoHT) have continuously been growing, and so it is important to develop robust countermeasures. However, there is a lack of publicly available datasets reflecting cyberattacks on IoHT, mainly due to privacy concerns. This paper showcases the development of a dataset, ECU-IoHT, which builds upon an IoHT environment having different attacks performed that exploit various vulnerabilities. This dataset was designed to help the healthcare security community in analyzing attack behavior and developing robust countermeasures. No other publicly available datasets have been identified for cybersecurity in this domain. Anomaly detection was performed using the most common algorithms, and showed that nearest neighbor-based algorithms can identify attacks better than clustering, statistical, and kernel-based anomaly detection algorithms.
Ahmed, M., Byreddy, S., Nutakki, A., Sikos, L. F., & Haskell-Dowland, P. (2021). ECU-IoHT: A dataset for analyzing cyberattacks in Internet of health things. Ad Hoc Networks, 122, article 102621. https://doi.org/10.1016/j.adhoc.2021.102621