Healthauth: A Multi-Modal Authentication System with ECC and Machine Learning for Healthcare Applications

Tamilselvan R, N Thangarasu

2025

Abstract

In the dynamic field of digital healthcare, effective user authentication is mandatory due to safeguarding critical health data and prevent any sort of unauthorized entry. This paper presents HealthAuth, a unique multimodal authentication approach that is powered by Elliptic Curve Cryptography (ECC) and machine learning to deliver robust and reliable security for healthcare-oriented solutions. It is a multi-layer comprehensive set using biometric (such as facial recognition, fingerprints) and behavioral (such as typing rhythm, user interaction) data for authentication, as seen in the proposed system. We propose a Structured Convolutional Neural Network (S-CNN) to improve the processing of biometric data, a kind of CNN architecture designed for health authentication tasks. The S-CNN is responsible for extracting hierarchical spatial features from biometric inputs, thus providing improved accuracy and also efficiency in feature extraction. Temoral Patterns are modelled using a Recurrent Neural Network (RNN) which makes it more secure for user behaviour. This securing the Authentication with ECC makes it light weight and very secure suitable for Healthcare IoT Devices, as well as its security as High because of performing challenge response mechanisms. HealthAuth combines cryptographic measures and classification via deep learning to ensure not only that the user is who they claim, but also how they are demonstrating themselves in real time, making a difficult target for spoofing or replay attacks. The arbitrary experiments exhibited that HealthAuth as a system performs better than conventional strategies as far as confirmation exactness, handling length, and security dangers which make it an ideal answer for guaranteeing secure access to EHRs, telemedicine stages, unified medicinal services gadgets.

Paper Citation

in Harvard Style

R T. and Thangarasu N. (2025). Healthauth: A Multi-Modal Authentication System with ECC and Machine Learning for Healthcare Applications. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 589-597. DOI: 10.5220/0013632700004664

in Bibtex Style

@conference{incoft25,
author={Tamilselvan R and N Thangarasu},
title={Healthauth: A Multi-Modal Authentication System with ECC and Machine Learning for Healthcare Applications},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={589-597},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013632700004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Healthauth: A Multi-Modal Authentication System with ECC and Machine Learning for Healthcare Applications
SN - 978-989-758-763-4
AU - R T.
AU - Thangarasu N.
PY - 2025
SP - 589
EP - 597
DO - 10.5220/0013632700004664
PB - SciTePress