Abstract
Visualising complex data facilitates a more comprehensive stage for conveying knowledge. Within the medical data domain, there is an increasing requirement for valuable and accurate information. Patients need to be confident that their data is being stored safely and securely. As such, it is now becoming necessary to visualise data patterns and trends in real-time to identify erratic and anomalous network access behaviours. In this paper, an investigation into modelling data flow within healthcare infrastructures is presented; where a dataset from a Liverpool-based (UK) hospital is employed for the case study. Specifically, a visualisation of transmission control protocol (TCP) socket connections is put forward, as an investigation into the data complexity and user interaction events within healthcare networks. In addition, a filtering algorithm is proposed for noise reduction in the TCP dataset. Positive results from using this algorithm are apparent on visual inspection, where noise is reduced by up to 89.84%.
Highlights
Hospital infrastructures are classified as mission-critical infrastructures [1]
This paper presents an investigation in to health care data patterns using a real-world hospital dataset
Healthcare data is intrinsically valuable; the repercussions of data compromise within healthcare infrastructures can range from loss of patient privacy and fraud, to patient injury or potentially death
Summary
Hospital infrastructures are classified as mission-critical infrastructures [1]. These information infrastructures have become increasingly dependent on information and communication technologies (ICT) to facilitate communication and automate services [2]. The goal of security engineers is to develop tools capable of detecting malicious, multi-stage intrusion attacks, weighting the individual attacks, and comparing them against the enormous and disparate database of attacks within the network [12]. This is a ‘plain recognition’ problem and an intruder’s objectives should be determined based on the analysis of the entire dataset of attacks as a whole, rather than just an individual attack [13]. A background discussion is put forward on the layout of medical infrastructures and the existing healthcare network security challenges. This background aids with the development of an approach to understanding the overall network behaviour
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