## Proposed User Verification Model
In the hunt for achieving higher accuracy, various other machine learning models were employed, however, the best results from [PERSON]'s analysis could not be beaten for each activity. Similarly, when deep learning models were trained on WISDM data ([[https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+](https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+)]([https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+](https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+))), high accuracy for every activity could not be produced due to factors such as the size of the dataset (when factored for each activity). The proposed user verification algorithm as shown in Figure 1 is a multi-step process, which can easily be translated as a part of the multi-factor authentication system. Users must first record their behavioural activity, just as they must with any other kind of authentication (facial or fingerprint), where the users first record their features in order to use them for authentication. Thereafter, they can perform the recorded activity when their device needs to verify their identity. The \"on demand\" in Figure 1 represents an event when the device needs to verify the user. The algorithm contains two machine learning models, for identification and authentication. The identification model is different for each activity, whereas the authentication model is different for each activity as well as for every user. The idea is to identify the user and then verify the identification by confirming that it can be differentiated from an impostor. It has also been taken into consideration that the machine learning model cannot always be trusted; however, developing a completely reliable model is a work in progress. As per our hypothesis, we have identified a threshold value for every model (each activity has a different model), whenever the probability score of a model's prediction, i.e., identification or authentication (identification and authentication have been modelled as classification problem) is equal to or greater than the threshold value, the model can be trusted with behavioural biometrics and proceed for verification. If this is not the state, it is safer to continue using normal means such as OTP or verification code to verify the user. Figure 1: User verification algorithm. However, integrating this algorithm as a part of a real-time user authentication system requires it to be secure and be able to enhance the reliability of the overall system. Although the system hosting the machine learning models may not be easily accessible by the intruder, the data used to train the models is comparatively easier to dupe, which, when perturbed, will significantly impact the performance of the models. Therefore, in order to assess the vulnerability of these machine learning models to attacked or altered data, the model was tested for black-box adversarial attacks. The WISDM data ([[https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+](https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+)]([https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+](https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+))) was attacked with a zero-order optimisation attack and, as predicted, the accuracy of the model was remarkably affected. On investigating the misclassifications further, it was found that the model was misclassifying the samples with a very low confidence score as compared to the classifications on the original data, and hence it was ruled that the model cannot be trusted with adversarial data due to its low confidence and prediction errors.