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Machine Learning-Based Cyber Intrusion Detection System for Internet of Medical Things Attacks in Healthcare Environments
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Author(s): Bhawnesh Kumar (Graphic Era University (Deemed), India), Ashwani Kumar (Shri Ram Group of Colleges, India), Harendra Singh Negi (Graphic Era University (Deemed), India)and Javed Alam (Quantlase Lab, Abu Dhabi, UAE)
Copyright: 2023
Pages: 15
Source title:
Cyber Trafficking, Threat Behavior, and Malicious Activity Monitoring for Healthcare Organizations
Source Author(s)/Editor(s): Dinesh C. Dobhal (Graphic Era University (Deemed), India), Sachin Sharma (Graphic Era University (Deemed), India), Kamlesh C. Purohit (Graphic Era University (Deemed), India), Lata Nautiyal (University of Bristol, UK)and Karan Singh (Jawaharlal Nehru University, India)
DOI: 10.4018/978-1-6684-6646-9.ch002
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Abstract
In this chapter, the authors calculate the accuracy value of machine learning models for combined, network, bio-medical data. The result shows that random forest has the highest accuracy value 94.17% for combined and 93.19% bio-medical data. For network data, decision tree classifier provides the highest accuracy value which is 94.07% whereas decision tree regression gives the highest accuracy value: 94.62% for combined, 92.11% for bio-medical, and 94.09% for network data.
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