Anomalous Network Behaviour Detection in Interoperable Health Systems using Machine Learning in Resource Limited Areas

Marseline Michael Mtey; Anael Elikana Sam; Mussa Ally Dida

doi:10.36948/ijfmr.2024.v06i04.26455

Anomalous Network Behaviour Detection in Interoperable Health Systems using Machine Learning in Resource Limited Areas

Author(s)	Marseline Michael Mtey, Anael Elikana Sam, Mussa Ally Dida
Country	Tanzania
Abstract	The connection of devices in distributed environments produces and shares a vast amount of data useful for different organisational decision-making. In healthcare service organisations, for example, multiple e-health systems from different departments or facilities connect and share health data and information. During sharing, proper management is important to ensure the information is secure against intruders. Machine learning as a non-conventional security technique can be used along conventional techniques like firewalls, antivirus and intrusion detection systems to predict future network threats and other anomalies using historical backgrounds and other features. However, some machine learning algorithms have complex computation thus requiring resourceful systems in terms of network bandwidth, CPU power, memory, and storage capacity. In resource-constrained environments, therefore, special consideration is needed to ensure that the analysis of the big data is successful and that the benefits associated with them are effectively obtained. In this paper, a Machine Learning algorithm was selected among four algorithms whose performance was compared through various performance metrics. Classification accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Absolute Error (RAE) among other performance metrics were used to compare the ANN, Random forest, Decision trees, and Naïve byes classification algorithms using an extract from CICDDOS2019 dataset. Using the Weka version 3.8.6, the algorithms were compared to choose the best one to classify the data. By using three computers with different resources, the experiments were carried out to determine the performance of those machine learning algorithms. The result revealed that the random forest produced a good average classification performance in resource-limited systems since it surpassed other algorithms in classifying the data at an average of 99 per cent with a low average mean absolute error of 0.0001. Furthermore, as an ensemble that classifies with multiple decision trees algorithm, it likewise uses reasonable time to build and test the model therefore recommended for resource-limited systems.
Keywords	Machine learning algorithms, Interoperability, Big data, Security, Anomalous behaviour, Resource limited areas, e-health system, Tanzania, Data Sharing
Field	Computer > Network / Security
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-08-26
Cite This	Anomalous Network Behaviour Detection in Interoperable Health Systems using Machine Learning in Resource Limited Areas - Marseline Michael Mtey, Anael Elikana Sam, Mussa Ally Dida - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.26455
DOI	https://doi.org/10.36948/ijfmr.2024.v06i04.26455
Short DOI	https://doi.org/gt8g5b

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Anomalous Network Behaviour Detection in Interoperable Health Systems using Machine Learning in Resource Limited Areas

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