ECU-IoHT: A dataset for analyzing cyberattacks in Internet of health things

Author Identifier

Mohiuddin Ahmed

ORCID : 0000-0002-4559-4768

Leslie Sikos

ORCID : 0000-0003-3368-2215

Paul Haskell-Dowland

ORCID : 0000-0003-1365-0929

Document Type

Journal Article

Publication Title

Ad Hoc Networks

Volume

122

Publisher

Elsevier

School

School of Science

RAS ID

36340

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

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.

DOI

10.1016/j.adhoc.2021.102621

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Link to publisher version (DOI)

10.1016/j.adhoc.2021.102621