Securing the Internet of Health Things: Embedded Federated Learning-Driven Long Short-Term Memory for Cyberattack Detection

Abstract

The proliferation of the Internet of Health Things (IoHT) introduces significant benefits for healthcare through enhanced connectivity and data-driven insights, but it also presents substantial cybersecurity challenges. Protecting sensitive health data from cyberattacks is critical. This paper proposes a novel approach for detecting cyberattacks in IoHT environments using a Federated Learning (FL) framework integrated with Long Short-Term Memory (LSTM) networks. The FL paradigm ensures data privacy by allowing individual IoHT devices to collaboratively train a global model without sharing local data, thereby maintaining patient confidentiality. LSTM networks, known for their effectiveness in handling time-series data, are employed to capture and analyze temporal patterns indicative of cyberthreats. Our proposed system uses an embedded feature selection technique that minimizes the computational complexity of the cyberattack detection model and leverages the decentralized nature of FL to create a robust and scalable cyberattack detection mechanism. We refer to the proposed approach as Embedded Federated Learning-Driven Long Short-Term Memory (EFL-LSTM). Extensive experiments using real-world ECU-IoHT data demonstrate that our proposed model outperforms traditional models regarding accuracy (97.16%) and data privacy. The outcomes highlight the feasibility and advantages of integrating Federated Learning with LSTM networks to enhance the cybersecurity posture of IoHT infrastructures. This research paves the way for future developments in secure and privacy-preserving IoHT systems, ensuring reliable protection against evolving cyberthreats.