ECU-IoHT: A dataset for analyzing cyberattacks in Internet of health things
Abstract
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.
RAS ID
36340
Document Type
Journal Article
Date of Publication
2021
Volume
122
School
School of Science
Copyright
subscription content
Publisher
Elsevier
Identifier
Mohiuddin Ahmed
ORCID : 0000-0002-4559-4768
Leslie Sikos
ORCID : 0000-0003-3368-2215
Paul Haskell-Dowland
ORCID : 0000-0003-1365-0929
Comments
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