## 2 Literature Review  
In recent years the user authentication model is gradually shifting from \"something the user knows\" to \"something the user is\". Behavioural biometrics (BB) and continuous authentication (CA) are used in this method [6; 7]. As mobile devices grow increasingly technologically capable, it is evident that the built-in sensors can be utilised to effectively capture most users' activity, enabling behavioural biometric user authentication. By continuously monitoring user behaviour and re-authenticating user identification throughout a session, CA technology adds an extra layer of protection to the original login procedure [8; 9]. Behavioural biometrics authentication approaches are based on a person's behavioural characteristics such as walking stride, touch gestures, keystroke dynamics, behaviour profiling, hand waving, and power consumption and fusion. However, due to some key flaws, such as the possibility of false positives/negatives, the balance between security and usability, privacy concerns, and so on, behavioural biometrics authentication is limited. To address these flaws, it is vital to increase accuracy and look into how to strike a balance between security and usability. Because users' habits and behaviour may vary over time, authentication systems must be able to adapt to these changes. It has also been studied that adversarial examples can be crafted for machine learning and deep learning models, which, when fed to these models, result in high misclassification errors [10]. Therefore, in this paper, we present a two-step motion-based user authentication model, which, although reliant on a pre-trained model, verifies the user each time. Since user verification is treated as a classification problem in this work, we also assess the model's vulnerability to adversarial examples and propose a defence strategy. Although there are a great number of studies proposing several different methods of identifying and verifying users based on their device interactions and the range of motion-based activities recorded by the sensors in their devices, the field of Continuous Authentication (CA) using behavioural biometrics needs a strategy to evaluate its efficiency and a framework to implement the theoretical background that has been developed as a result of vast research in the field. A viable framework for employing behavioural biometrics as a means for user authentication will not only boost a high degree of confidence in the field but also help produce results that match the expectations and requirements of authentication systems. Most of the observations made with behavioural biometrics support the hypothesis that a person's behavioural profile can be developed by utilising their device usage/interaction patterns. However, this has also been a consistent point of scepticism, as human behaviour tends to change or evolve with time. This work also talks about the prospect of how behavioural biometrics can adapt to the changes in human behaviour with time. It is also important to consider the risks that come with using behaviour profiles and motion-based biometrics, apart from the data collection and its safe and ethical usage, as human behaviour can be imitated. [PERSON] et al. [7; 11] talks about the various kinds of zero-effort attacks that can be planted in a behavioural biometrics system. The attacker does not take any complex action in the zero-effort attack. It is predicated on the attacker's and legitimate user's templates being sufficiently similar, and it is related to the 'uniqueness' property of a biometric trait. An adversary attack entails the attacker doing specific actions in order to convincingly mimic a real user. The amount of sophistication of an adversary attack is highly dependent on the resources available, such as digital or physical means, time, and information about the biometric system and the victim. This reinforces the importance of real-time monitoring and evaluation of the results produced by the underlying machine learning/deep learning models in these methods.