Year: 2024
Type: article
Abstract: In a simulated healthcare setting, the algorithms were assessed based on organized threat insight data, inconsistency location executed with blockchain-enhanced access control, and machine learning-driven interruption detection. The test results depiction showed that all calculations were feasible, with an accuracy range of 0.88-0.94 and lift defined between 0.75 and 1; knowledge values ranging from.86 to.92, and F1 scores between and above.90 results are displayed as follows: Above all, TIAA excelled in risk insights management; ADA exceeded expectations in detecting inconsistencies; BACA used blockchain to fortify access control; and ML-IDS produced remarkable results in intrusion detection. The importance of these algorithms in addressing particular cybersecurity concerns in the healthcare industry is highlighted through a comparative comparison with similar studies. The suggested algorithms are relevant to the growing conversation about cybersecurity in healthcare because they offer a comprehensive strategy to protect private health data, guarantee the reliability of assessment models, and fortify organizations against a variety of evolving cyberthreats.
DOI: 10.22399/ijcesen.596
Authors: M. Husain Bathushaw, S. Nagasundaram
Institution: Vels University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404665382