# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 2 Background and Related Works  
Biometric authentication has grown in popularity with the effective use and adoption of IoT networks, especially in PH [2]. Many continuous authentication techniques have recently emerged to offer more reliable solutions to the growing challenges. In continuous authentication, users are monitored frequently to validate their authority for a particular session [5]. Compared with the traditional authentication process, continuous authentication mechanisms are equipped with a higher level of security and are well-known for enhanced Quality of Experience (QoE) [6]. Continuous authentication incorporates behavioural and physiological signal-based biometric authentication, which can make the IoT networks more secure, which are prone to impersonation and injection attacks [7]. The use of IoT sensors in any system poses serious security challenges [2]. The PH systems which are heavily dependent on such sensors are not exceptional. Figure 4 summarises the main security threats that IoT device layer authentication techniques can face in such contexts. 1. **Device impersonation attack:** The attacker impersonates a device pretending to be an authenticated user [8];
2. **Injection attack:** The attacker deploys malicious nodes to monitor or control the data flows in the network [9];
3. **Side-channel attack:** The attacker reads the leaked signals from a device to collect and analyse sensitive data [10];
4. **Eavesdropping and interference:** This type of attack is caused by a weakened connection between an IoT device and the server. The intruder takes advantage of such a vulnerable connection to intercept network traffic [11];
5. **Sleep deprivation attack:** The attacker keeps the targeted node out of its sleep mode to reduce its lifetime [12];
6. **DDoS attack:** The attackers generate a large amount of traffic from compromised devices to make services unavailable [2];
7. **Replay attack:** The imposter produces a signal to control IoT devices [2];
8. **Man in the middle attack:** The attacker hacks the communication channel between two nodes and spoofs or interrupts communications [2]. Many studies have focused on biometric behavioural and physiological authentication in IoT healthcare networks since wearable and inertial sensors are gaining popularity. One of the critical advantages of physiological signals over physiological features is their sense of life [13]. ECG-based authentication has also gained growing interest, particularly for continuous authentication scenarios [14]. For instance, the work in [15] proposed an ECG-based cancelable biometric scheme that mitigates replay attacks. In [16], the authors proposed a continuous authentication mechanism that incorporates sequential sampling and One-Dimensional Multi-Resolution Local Binary Patterns extraction to identify usersover using ECG signals. The work in [17] defined a unified sparse representation framework that uses joint and specific ECG signal patterns. ECG-based authentication mechanisms can be further classified based on feature extraction techniques. For instance, studies such as [1,18] have applied fiducial feature extraction in their identification algorithms. Many other studies [19,20], on the other hand, have used non-fiducial feature extraction techniques. Other studies, such as [21,22], have combined the benefits of fiducial and non-fiducial extraction to form a hybrid approach for ECG-based authentication. Some work, such as [23] focused on investigating various classification algorithms to identify the best performing one for ECG-based authentication. Out of eight algorithms, the study concluded that linear discriminant analysis (LDA), k-nearest neighbours, and neural networks are best for ECG-based identification. Furthermore, it also concluded that principal components analysis (PCA) has no noticeable impact on the classification process performance but can lead to accuracy reduction. A few studies have proposed PPG-based authentication methods.