A Privacy-Enhanced Federated Intrusion Detection System for Healthcare Networks with Differential Privacy Guarantees

Prashant Singh Rana

A Privacy-Enhanced Federated Intrusion Detection System for Healthcare Networks with Differential Privacy Guarantees

Authors

Prashant Singh Rana

Thapar Institute of Engineering, & Tech, India

Keywords:

Federated Learning, Differential Privacy, Intrusion Detection System, Healthcare Security, Privacy Preservation, Cybersecurity

Synopsis

Intrusion Detection Systems (IDS) are essential in protecting healthcare networks from cyber threats. However, centralized IDS architectures raise privacy concerns due to the sensitive nature of patient data. This paper presents a Federated Learning (FL)-based Intrusion Detection System, integrated with Differential Privacy (DP) to preserve data confidentiality while ensuring detection efficiency. We evaluate our model on two publicly available healthcare cybersecurity datasets, showing significant improvements in detection accuracy and privacy preservation. Simulation results demonstrate our system's feasibility for real-time deployment in decentralized healthcare infrastructures.

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