@phdthesis{Buech2019, type = {Master Thesis}, author = {Holger B{\"u}ch}, title = {Continuous Authentication using Inertial-Sensors of Smartphones and Deep Learning}, url = {https://nbn-resolving.org/urn:nbn:de:bsz:900-opus4-65060}, pages = {119}, year = {2019}, abstract = {The legitimacy of users is of great importance for the security of information systems. The authentication process is a trade-off between system security and user experience. E.g., forced password complexity or multi-factor authentication can increase protection, but the application becomes more cumbersome for the users. Therefore, it makes sense to investigate whether the identity of a user can be verified reliably enough, without his active participation, to replace or supplement existing login processes. This master thesis examines if the inertial sensors of a smartphone can be leveraged to continuously determine whether the device is currently in possession of its legitimate owner or by another person. To this end, an approach proposed in related studies will be implemented and examined in detail. This approach is based on the use of a so-called Siamese artificial neural network to transform the measured values of the sensors into a new vector that can be classified more reliably. It is demonstrated that the reported results of the proposed approach can be reproduced under certain conditions. However, if the same model is used under conditions that are closer to a real-world application, its reliability decreases significantly. Therefore, a variant of the proposed approach is derived whose results are superior to the original model under real conditions. The thesis concludes with concrete recommendations for further development of the model and provides methodological suggestions for improving the quality of research in the topic of \"Continuous Authentication\".}, language = {en} }