A Game Theoretic and Machine Learning Approach for Strengthening Network Security Using Honeypots in Healthcare

Fareed, Iqra (2024) A Game Theoretic and Machine Learning Approach for Strengthening Network Security Using Honeypots in Healthcare. Masters thesis, Dublin, National College of Ireland.

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Abstract

Detrimental effects of ransomware attacks on healthcare sectors continue to grow in prevalence, threatening patients, data privacy and essential operations in healthcare organisations. This research focused on establishing the optimum defence strategy for protecting healthcare networks against ransomware attacks with the help of machine learning, honeypots, and game theory. Using Gradient Boosting Machines (GBMs), the machine learning model is employed to predict the presence of ransomware by learning and analysing features in the network traffic. Moreover, the framework of Bayesian game theory was employed to improve and evolve network defence dynamically depending on probabilistic threats. These complex technologies have aimed to provide an intelligent and dynamic security environment to counter ransomware threats efficiently besides detecting them. It obtained a high detection rate of ransomware, demonstrated the utility of honeypot in acquiring information on attack behaviours, and the real-time reinforcement of defence mechanisms.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Vikas UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning G Geography. Anthropology. Recreation > GV Recreation Leisure > Games and Amusements > Online Games
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	29 Jul 2025 11:52
Last Modified:	29 Jul 2025 11:52
URI:	https://norma.ncirl.ie/id/eprint/8308

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