Mouse Authentication Without the Temporal Aspect – What Does a 2D-CNN Learn?

Penny Chong, Yi Xiang Marcus Tan, Juan Guarnizo, Yuval Elovici, Alexander Binder
2018 2018 IEEE Security and Privacy Workshops (SPW)  
Mouse dynamics as behavioral biometrics are under investigation for their effectiveness in computer security systems. Previous state-of-the-art methods relied on heuristic feature engineering for the extraction of features. Our work addresses this issue by learning the features with a convolutional neural network (CNN), thereby eliminating the need for manual feature design. Contrary to time-series-based modeling approaches, we propose to use a two-dimensional CNN with images as inputs. While
more » ... unterintuitive at first sight, it permits to profit from well-initialized lower-layer kernels obtained via transfer learning. We demonstrate our results on two public datasets, Balabit and TWOS, and compare against a 1D-CNN and a classical baseline relying on hand-crafted features, which are both outperformed. We show that a positionindependent variant of the 2D-CNN loses little performance yet we learned that the trained classifier is very sensitive to simulated resolution shifts at test time. In a final step, we analyze and visualize the learned features on single test curves using layer-wise relevance propagation (LRP). This analysis reveals that the 2D-CNN uses curve information only sparsely, with a tendency to assign little relevance to straight segments and artifactual curve crossings.
doi:10.1109/spw.2018.00011 dblp:conf/sp/ChongTGEB18 fatcat:xljls4unizga5gkb75hmwijr7e