000			a
999			_c22571 _d22571
003			OSt
005			20250403095350.0
008			250403b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_925770 _aAkinola, Omolola
245			_a Intelligent threat detection and response systems for safeguarding cloud-hosted electronic health records from cyber attacks
250			_aVol.14(4), Jul-Aug
260			_aHaryana _bIOSR - International Organization of Scientific Research _c2024
300			_a1-10p.
520			_aAs more people use cloud-based electronic health record (EHR) systems, they make healthcare better, but they also make it easier for cybercriminals to attack. This article describes a system that uses artificial intelligence (AI) and machine learning (ML) to intelligently watch cloud-hosted EHR environments for bad behaviour, find cyberattacks, and automatically take the right steps to stop them. Using supervised machine learning models that have been trained on known threat indicators, the suggested framework constantly looks at log and system data. As soon as an attack is found, established containment and mitigation steps are carried out naturally to lower the harm. The test results show that the framework can correctly identify common EHR attack methods like ransomware and data theft, as well as quickly and effectively protect private patient data.
650		0	_94619 _aEXTC Engineering
773	0		_tIOSR journal of VLSI and signal processing (IOSR-JVSP) _dGurgaon International Organization Of Scientific Research (IOSR) _x2319 – 4197
856			_uhttps://www.iosrjournals.org/iosr-jvlsi/papers/vol14-issue4/A14040110.pdf _yClick here
942			_2ddc _cAR