Title: Web-based botnet for blocking control flow in open-source medical syringe pump

Authors: Wei Lu

Addresses: Department of Computer Science, Keene State College, University System of New Hampshire, Keene, New Hampshire, USA

Abstract: Integrating open-source medical systems, with advancements in 3D printing technology and microcomputer systems such as Arduino and Raspberry Pi, has revolutionised the healthcare industry. However, it has also exposed cybersecurity vulnerabilities in hospitals. This paper presents a web-based botnet as a proof-of-concept to demonstrate potential disruptions in the control flow of a syringe pump in an IoT medical network testbed. Our lightweight botnet stands out for its rapid deployment and minimal use of resources. We also provide a publicly available data set from this botnet for cybersecurity research on open-source medical systems. Additionally, we developed a methodology for feature selection to detect botnet attacks. Our comparative study with various machine learning algorithms revealed the best strategy for detecting these attacks using network traffic data from benign and malicious environments. The results were impressive, with our feature selection technique achieving over 99% accuracy on the testing data set, successfully identifying 63,380 out of 63,382 attack instances.

Keywords: internet of medical things; machine learning; botnet; denial of service.

DOI: 10.1504/IJGUC.2025.145175

International Journal of Grid and Utility Computing, 2025 Vol.16 No.2, pp.162 - 172

Received: 29 Oct 2023
Accepted: 22 Mar 2024
Published online: 24 Mar 2025 *

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