Cited by A context-aware zero trust-based hybrid approach to IoT-based self-driving vehicles security

Enhancing healthcare data privacy and interoperability with federated learning DOI

Adil Akhmetov,

Zohaib Latif,

Benjamin Tyler

et al.

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2870 - e2870

Published: May 8, 2025

This article explores the application of federated learning (FL) with Fast Healthcare Interoperability Resources (FHIR) protocol to address underutilization huge volumes healthcare data generated by digital health revolution, especially those from wearable sensors, due privacy concerns and interoperability challenges. Despite advances in electronic medical records, mobile applications, current cannot fully exploit these lack analysis exchange between heterogeneous systems. To this gap, we present a novel converged platform combining FL FHIR, which enables collaborative model training that preserves sensor while promoting standardization interoperability. Unlike traditional centralized (CL) solutions require centralization, our uses local learning, naturally improves privacy. Our empirical evaluation demonstrates models perform as well as, or even numerically better than, terms classification accuracy, also performing equally regression, indicated metrics such area under curve (AUC), recall, precision, among others, for classification, mean absolute error (MAE), squared (MSE), root square (RMSE) regression. In addition, developed an intuitive AutoML-powered web is CL compatible illustrate feasibility predictive modeling physical activity energy expenditure, complying FHIR reporting standards. These results highlight immense potential FHIR-integrated practical framework future interoperable privacy-preserving ecosystems optimize use connected data.

Language: Английский

Citations

Cybersecurity in maritime power systems: A comprehensive review of cyber threats and mitigation techniques DOI

V.T. Mai,

Ardashir Mohammadzadeh, Khalid A. Alattas

et al.

Electric Power Systems Research, Journal Year: 2025, Volume and Issue: 247, P. 111797 - 111797

Published: May 8, 2025

Language: Английский

Citations

LSF-IDM: Deep learning-based lightweight semantic fusion intrusion detection model for automotive DOI

Pengzhou Cheng, Lei Hua, Haobin Jiang

et al.

Peer-to-Peer Networking and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: June 6, 2024

Language: Английский

Citations

DeepDeter: Strengthening Cybersecurity Against DoS Attacks with Deep Learning DOI

Vanshika Pahuja,

Sharad Shyam Ojha

Published: March 15, 2024

Ever-evolving nature of cyber world has led to the significant increase in different types network attacks. Deep learning algorithms can be used for detection such attacks which attackers have send many requests on server and flooded it with ample data. Many packets been sent targeted system unavailability online systems crashing sites. These cause huge amount loss will become inoperative. This research work deploys deep techniques purpose removal DoS these are Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) Gated Unit. all capturing sequential data time-series analyse identify patterns as associate LSTM outperforms best among but this model gives highest accuracy 92.3% indicate more superior ability properly classification instances attack traffic at server.

Language: Английский

Citations

A context-aware zero trust-based hybrid approach to IoT-based self-driving vehicles security DOI

Izhar Ahmed Khan, Marwa Keshk, Yasir Hussain

et al.

Ad Hoc Networks, Journal Year: 2024, Volume and Issue: unknown, P. 103694 - 103694

Published: Oct. 1, 2024

Language: Английский

Citations