# Continuous Mobile User Authentication Using Combined Biometric Traits
## 8 Conclusions  
The goal of the work was to experiment with continuous mobile user authentication for Android devices using HMM classifiers. As we can see from the results presented in Section 6, the performance of the classifier is highly influenced by the features used for the training and verification process. The internal configuration of the generated model, which is determined using the Baum-Welch expectation maximization algorithm, also influences the prediction results. Because this algorithm converges to a local optimum, the outcome may differ after training with the same dataset. This also introduces a problem when the number of hidden states and mixtures is set too high, in overfitting the model to the trained dataset. Since single gesture inputs result in a random outcome of the prediction algorithm, our work illustrates how the grouping of multiple gestures results in better performance but also introduces problems for the overall security of the system. Furthermore, the restriction of the gesture length used for classification was evaluated. This did also improve the performance of the classifier in some configurations, but did introduce similar problems as the approach of grouping gesture scores. Our model achieving the best results used six states and seven mixtures predicting on position and accelerometer data. With a group of ten gestures, limited to ones with more than 50 input points, the classifier reaches an AUC of 97% and an EER of 6.91%. If the number of grouped gestures is increased to 25, an AUC of 100% and an EER of 1.34% can be achieved. A comparison to already existing systems indicates that despite reaching good performance metrics, implementations based on other classifiers and a more diverse feature set achieve better results. On the other hand, our system provides more in-depth insight on a fully integrated system, from collecting the necessary data, to adapting the trained model and finally predicting on those values. For future work, an integration of the model training component into an Android application would be interesting. This would result in a fully integrated authentication framework, based on a single system, where studies on user acceptance or similar could be made. Conceptualization, E.S. ; Data curation, D.R. ; Funding acquisition, M.K. ; Resources, M.K. ; Supervision, E.S. ; Validation, M.K. ; Writing--original draft, D.R. ; Writing--review & editing, E.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The project data mentioned in this paper are available at [[https://github.com/dominik013/continuous-mobile-user-authentication](https://github.com/dominik013/continuous-mobile-user-authentication)]([https://github.com/dominik013/continuous-mobile-user-authentication](https://github.com/dominik013/continuous-mobile-user-authentication)) (accessed on 6 December 2021). Open Access Funding by the University for Continuing Education Krems, the University of Applied Sciences BFI Vienna and the University of Applied Sciences Upper Austria. The authors declare no conflict of interest. The data and code used for the implementations of the recorder, model training as well as the verification component are available at the original authors Github: [[https://github.com/dominik013/continuous-mobile-user-authentication](https://github.com/dominik013/continuous-mobile-user-authentication)]([https://github.com/dominik013/continuous-mobile-user-authentication](https://github.com/dominik013/continuous-mobile-user-authentication)) (accessed on 4 December 2021).