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A Machine Learning-Based Framework for Intrusion Detection Systems in Healthcare Systems
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Author(s): Janmejay Pant (Graphic Era Hill University, India), Rakesh Kumar Sharma (Pal College of Technology and Management, Haldwani, India), Himanshu Pant (Graphic Era Hill University, India), Devendra Singh (Graphic Era Hill University, India)and Durgesh Pant (Uttarakhand Open University, Haldwani, India)
Copyright: 2023
Pages: 11
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.ch006
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Abstract
A reliable intrusion detection system is an important key component of healthcare-based systems. Intrusion detection systems are crucial in e-healthcare because patient medical records must be maintained accurately, safely, and secretly. Errors in diagnosis and therapy might result from changing the actual patient data. It is not possible to handle complex data using traditional techniques. Current network requirements cannot be met by diversified intrusion techniques. In addition to the rise in data, attacks are also escalating rapidly. The area of network security is trending when it comes to machine learning techniques. This study aims to develop a novel machine learning framework for detecting attacks.
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