Intelligent threat detection and response systems for safeguarding cloud-hosted electronic health records from cyber attacks
Publication details: Haryana IOSR - International Organization of Scientific Research 2024Edition: Vol.14(4), Jul-AugDescription: 1-10pSubject(s): Online resources: In: IOSR journal of VLSI and signal processing (IOSR-JVSP)Summary: As 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.| Item type | Current library | Status | Barcode | |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2025-0511 |
As 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.
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