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Behavioral Intrusion Prediction Model on Bayesian Network over Healthcare Infrastructure

Mohammad Hafiz Mohd Yusof1,*, Abdullah Mohd Zin2, Nurhizam Safie Mohd Satar2

1 Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia
2 Centre for Software Technology and Management (SOFTAM), Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor Malaysia

* Corresponding Author: Mohammad Hafiz Mohd Yusof. Email: email

Computers, Materials & Continua 2022, 72(2), 2445-2466. https://doi.org/10.32604/cmc.2022.023571

Abstract

Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common characteristics, namely lack of prior information (p (θ)), and reduced parameters (θ). Therefore, this study aims to utilize the previously built Feature Selection Model subsequently to design a Predictive Analytics Model based on Bayesian Network used to improve the analysis prediction. Feature Selection Model is used to learn significant label as a target and Bayesian Network is a sophisticated probabilistic approach to predict intrusion. Finally, the results are extended to evaluate detection, accuracy and false alarm rate of the model against the subject matter expert model, Support Vector Machine (SVM), k nearest neighbor (k-NN) using simulated and ground-truth dataset. The ground-truth dataset from the production traffic of one of the largest healthcare provider in Malaysia is used to promote realism on the real use case scenario. Results have shown that the proposed model consistently outperformed other models.

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Cite This Article

APA Style
Yusof, M.H.M., Zin, A.M., Satar, N.S.M. (2022). Behavioral intrusion prediction model on bayesian network over healthcare infrastructure. Computers, Materials & Continua, 72(2), 2445-2466. https://doi.org/10.32604/cmc.2022.023571
Vancouver Style
Yusof MHM, Zin AM, Satar NSM. Behavioral intrusion prediction model on bayesian network over healthcare infrastructure. Comput Mater Contin. 2022;72(2):2445-2466 https://doi.org/10.32604/cmc.2022.023571
IEEE Style
M.H.M. Yusof, A.M. Zin, and N.S.M. Satar "Behavioral Intrusion Prediction Model on Bayesian Network over Healthcare Infrastructure," Comput. Mater. Contin., vol. 72, no. 2, pp. 2445-2466. 2022. https://doi.org/10.32604/cmc.2022.023571



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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