Intrusion detection system in Healthcare ecosystem using customized neural network

The healthcare ecosystem is increasingly reliant on IoT devices for monitoring and managing patient health. However, the proliferation of connected devices introduces significant security vulnerabilities, making healthcare systems prone to cyberattacks, data breaches, and system intrusions. This project proposes an Intrusion Detection System (IDS) specifically tailored for the healthcare ecosystem, leveraging customized neural network architectures to identify and mitigate these security risks. The IDS analyzes real-time network traffic data generated by IoT devices within healthcare environments to detect anomalous behaviors indicative of cyber threats. The system uses a variety of machine learning algorithms such as Convolutional Neural Networks (CNN), XGBoost, and Random Forest to classify network traffic as either benign or malicious. With CNN's capability to capture intricate patterns in network traffic, the system ensures high accuracy in intrusion detection, even under complex attack scenarios. Additionally, the system provides actionable alerts, enabling timely response and mitigation measures to prevent potential security breaches. This project aims to enhance the security of healthcare systems by providing a robust, scalable, and intelligent intrusion detection mechanism, ensuring the integrity and confidentiality of patient data. The system is designed to be highly efficient, capable of handling the vast amounts of data generated in healthcare networks.

Keywords: Intrusion Detection, Healthcare Ecosystem, IoT Security, Neural Networks, Machine Learning, Convolutional Neural Networks (CNN), XGBoost, Random Forest, Cybersecurity, Data Protection.