Enhancing Cybersecurity in Healthcare Through AI-Driven Threat Detection and Cloud-Based Solutions

EasyChair Preprint 14535

Abstract

The increasing digitalization of healthcare has led to a surge in cybersecurity threats, making the protection of sensitive patient data a critical priority. This paper presents an AI-driven, cloud-based cybersecurity framework designed to detect and mitigate threats in real-time within healthcare systems. By leveraging advanced machine learning (ML) algorithms and cloud computing capabilities, this framework aims to enhance the security of healthcare networks, ensuring the confidentiality, integrity, and availability of data. A comprehensive evaluation of multiple ML models, including Decision Trees, Random Forests, Neural Networks, and Support Vector Machines, was conducted using a healthcare-specific dataset. The results demonstrate that AI-driven threat detection models significantly improve the accuracy and speed of threat identification compared to traditional methods. A comparative analysis with existing literature reveals the superior performance of the proposed framework in various cybersecurity scenarios.

Keyphrases: Artificial Intelligence, Cloud Computing, Cybersecurity, Healthcare, Threat Detection, data protection, machine learning

@booklet{EasyChair:14535,
  author    = {Matilda Bennett},
  title     = {Enhancing Cybersecurity in Healthcare Through AI-Driven Threat Detection and Cloud-Based Solutions},
  howpublished = {EasyChair Preprint 14535},
  year      = {EasyChair, 2024}}