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Securing Electronic Health Records against Insider-Threats: A Supervised Machine Learning Approach
  • William Hurst
William Hurst
Wageningen University and Research

Corresponding Author:[email protected]

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This article focuses on insider-threat detection within healthcare infrastructures. There are inherent dangers to digitising patient records and considering the sensitive nature of the data, EHR is equally at risk of both external threats and insider attacks, but security applications are predominantly facing the outer boundary of the network. In this work, the focus is on insider data misuse detection. The approach involves the use of supervised classification (decision tree, random forest and support vector machine) based off pre-labelled real world data collated from a UK-based hospital for the detection of EHR data misuse. The results demonstrate that by employing a machine learning approach to analyse EHR data access, anomaly detection can be achieved with a 0.9896 accuracy from a test set and 0.9908 from the validation set using a support vector machine classifier. The emphasis of this research is on the detection of EHR data misuse, through the detection of anomalous behavioural patterns. Based on the results, the recommendation is to adopt an SVM for data misuse/insider threat detection
Dec 2022Published in Smart Health volume 26 on pages 100354. 10.1016/j.smhl.2022.100354