1 Department of Computer Science, Edo State University, Uzairue, Nigeria.
2 Department of ICT Education, Africa Center of Excellence for Innovative and Transformation STEM Education (ACEITSE), Lagos State University, Lagos.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(01), 2813-2821
Article DOI: 10.30574/wjarr.2024.23.1.2155
Received on 11 June 2024; revised on 25 July 2024; accepted on 27 July 2024
Intrusion detection systems are increasingly becoming more and more useful in the fight against cyber threats. As businesses continue to transition into adopting information processing systems including cloud computing, there is a greater need for the development of safety measures to ensure the preservation of information against theft, unauthorized access and other cyber threats. Several articles in literature have reported the development of intrusion detection and prediction system using varying techniques. In this research work, a simple Bayesian model is presented. To build this model, the widely used dataset of NSL-KDD was used. Existing literature was relied on for the selection of the best features while the Max-Min Hill Climbing (MMHC) algorithm was used to define the Bayesian network structure. The model was then implemented using the Genie Bayesian Modelling software. Results of simulation, influence and scenario analysis established the efficacy of the model in predicting the likelihood of an intrusion in a network environment.
Intrusion detection; Bayesian Network; Cyber security; Machine Learning; Information Systems
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Ijegwa David Acheme, Adebanjo Adeshina Wasiu and Glory Nosa Edegbe. A network intrusion prediction model using Bayesian network. World Journal of Advanced Research and Reviews, 2024, 23(1), 2813-2821. Article DOI: https://doi.org/10.30574/wjarr.2024.23.1.2155