# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 1 Introduction
Such contexts also advocate for a robust _continuous authentication_ method to scrutinise a group of persons and their sensors belonging to a specific network. In this paper, to address the abovementioned challenges, we propose a multimodal biometric-based authentication model that comprises continuous single and group user and device authentication in PH network environments. We utilise a person's unique physiological characteristics that are continuously monitored using various IoT sensors to classify real users and intruders. Our method aims to overcome the potential security flaws in such sensors by employing a person's biological features that can be very difficult to borrow, buy or forge. To achieve this, the performance of the multimodal authentication method over a single modal is empirically investigated and analysed. Single modal authentication utilises and considers a user's single biometric trait (e.g., ECG), which is still feasible to be compromised given the advancement in computation [3]. Unlike single signal modal, multimodal authentication considers more than one characteristic of the human body while processing a verification process. Such approaches can result in higher accuracy and a higher security level, making spoofing, device impersonation and replay attacks harder to occur [4]. Therefore, we devise a multimodal individual and group-based biometric authentication, combining features among a few common biometric traits such as ECG and PPG. The systematic approach investigates both user and device authenticity. Furthermore, we employ various machine learning algorithms and empirically evaluate the performance of our proposed method. The objectives and novel contributions of our work are:  
* We propose the first comprehensive deep learning-based multimodal authentication model applying ECG and PPG signals fusion. Our objective is to prevent user impersonation and device spoofing in the personalised healthcare network, heavily dependent on IoT-based sensors;
* Our model incorporates a two-level authentication to detect intruding users and devices. The developed approach first enrols the authorised users using their multiple biometric traits. It then attaches IoT sensors using those traits to an individual. Such methods prevent the impersonation of both users and devices. It can easily detect the presence of an intruder in IoT-based healthcare services;  
Figure 3: Scenario: Group User Authentication. * We have conducted extensive experiments over multiple public datasets, demonstrating the model's efficiency for biometric authentication purposes, compared to the previous research works that have used most of these datasets for clinical research perspectives;
* To the best of the authors' knowledge, very few works have addressed the device-level authentication and audit mechanisms of IoT-based personal healthcare services. The rest of this paper is organised as follows. In Section 2, we present background and motivation for multimodal continuous authentication method. The related works are also described with a concise view. In Section 3, the overall system architecture and relevant algorithms and models are narrated. In Section 4, multiple types of attack scenarios are discussed concerning biometric-based authentication. In Section 5, details of the dataset, experiments and the results are presented, followed by a discussion of the findings in Section 6. Finally, Section 7 illustrates the limitation of our work and Section 8 summarises the objective, outcome, and future direction of this work.