# A Comprehensive Survey on Intrusion Detection Systems for Healthcare 5.0: Concepts, Challenges, and Practical Applications

## TL;DR

This paper reviews intrusion detection systems for Healthcare 5.0, emphasizing AI models, datasets, and explainability to improve security in smart healthcare.

## Contribution

The paper offers an integrated analysis of IDSs in Healthcare 5.0, bridging cybersecurity and clinical practice.

## Key findings

- Fusing network and biomedical features improves threat detection in Healthcare 5.0 systems.
- Physiological signals are crucial for identifying complex attacks like spoofing.
- Explainable AI is essential for transparency in healthcare cybersecurity.

## Abstract

Healthcare 5.0 represents the next evolution in intelligent and interconnected healthcare systems, leveraging emerging technologies such as Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) to enhance patient care and automation. While Intrusion Detection Systems (IDSs) are a critical component for securing these environments, the current literature lacks a systematic analysis that jointly evaluates the effectiveness of AI models, the suitability of datasets, and the role of Explainable Artificial Intelligence (XAI) in the Healthcare 5.0 landscape. To fill this gap, this survey provides a comprehensive review of IDSs for Healthcare 5.0, analyzing state-of-the-art approaches and available datasets. Furthermore, a practical case study is presented, demonstrating that the fusion of network and biomedical features significantly improves threat detection, with physiological signals proving crucial for identifying complex attacks like spoofing. The primary contribution is therefore an integrated analysis that bridges the gap between cybersecurity theory and clinical practice, offering a guide for researchers and practitioners aiming to develop more secure, transparent, and patient-centric systems.