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behavioral Intrusion Prediction Model on Bayesian Network over Healthcare Infrastructure

Computers, Materials & Continua 2022年第8期72卷 2445-2466页

作者： Mohammad Hafiz Mohd Yusof Abdullah Mohd Zin Nurhizam Safie Mohd Satar Faculty of Computer&Mathematical Sciences Universiti Teknologi MARA40450Shah AlamSelangorMalaysia Centre for Software Technology and Management(SOFTAM) Universiti Kebangsaan Malaysia43600BangiSelangor Malaysia

Due to polymorphic nature of malware attack,a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature ofmalware *** the other hand,state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised *** is unlikely to be the case in production network as the dataset is unstructured and has no *** an unsupervised learning is *** study is one of the techniques to elicit traffic ***,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 *** Selection Model is used to learn significant label as a target and Bayesian Network is a sophisticated probabilistic approach to predict ***,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 *** 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 *** have shown that the proposed model consistently outperformed other models.

关键词： Intrusion detection prevention system behavioral malware analysis machine learning in cybersecurity deep learning in intrusion detection system(IDS)and intrusion prevention system(IPS)

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