## 3 Literature Review
### Behavioral Biometrics
#### 3.1.2 Motion Dynamics
Their model produced the best authentication metrics of 1.00% EER, 1.42% false acceptance rate (FAR), and 0.75% false rejection rate (FRR). Studies [37, 38, 39, 40] used RNNs such as LSTM for their authentication models. AutoSen, a continuous mobile authentication model utilizing motion sensor data, was proposed in [37]. For this model, data were captured implicitly without constraints on activity to preserve a realistic authentication scenario. Their dataset consisted of motion data from five smartphone sensors collected from 84 participants. An LSTM-RNN was used to capture behavioural traits directly from this dataset. Three different LSTM architectures were compared, including simple LSTM, bidirectional LSTM, and multilayer LSTM. Their analysis found that using data from only three sensors (accelerometer, gyroscope, and magnetometer) produced the best results. Multilayer LSTM was used for further authentication analysis and was able to achieve FRR 0.96%, FRR 8.08%, and EER 0.09% at 0.5 s frequency intervals. In study [38], DeepAuthen was proposed, utilizing motion sensor data for continuous authentication. Their model utilized DeepConvLSTM, a hybrid architecture consisting of a three-layer CNN tasked to handle spatial data and a one-layer LSTM to handle temporal data. Motion sensor data from the HMOG, UCI-HAR, and WISDM-HARB datasets were used to train and test their model. For evaluation, their CNN + LSTM model was compared to individual CNN and LSTM models. DeepConvLSTM outperformed the baseline models with a highest accuracy of 99.99% (\(\pm\)0.030%) and an EER of 0.01% (\(\pm\)0.060%) in the navigation during a sitting scenario from the HMOG dataset. Researchers of [39] proposed DeepAuth, an LSTM-based continuous authentication scheme using passive user behavior while online shopping. For their dataset, motion sensor data were collected from 47 users while browsing the Target application. Discrete Fourier transform (DTF) was then used to convert time signal data into frequency signals. An LSTM received these data for authentication. Their LSTM model was compared to four other baseline models: SVM, RF, logistic regression (LR), and GBC. Results found that DeepAuth outperformed the baselines and was able to detect both micro- and macro-movement patterns. Their highest metrics were 92.73% negative accuracy, 99.20% positive accuracy, 95.85% F1 score, and 99.05% AUC. In [40], researchers detailed a large-scale study on deep learning with human kinematics, proposing an active mobile authentication scheme via motion sensors. Their model used a specialized shift-invariant dense clockwork RNN for feature extraction and a GMM for classification. To test their model, researchers in this study created a dataset consisting of unrestricted motion sensor data collected from 1500 volunteers. When compared with other temporal models, their dense clockwork RNN produced the best accuracy with GMM. Figure 2: Proposed CNN + SVM architecture [35]. The best metrics from their authentication evaluation included an EER of 18.17% and a human-targeted translation error rate (HTER) of 19.29%. Researchers of [41, 42] used other deep learning algorithms in their models. In [41], researchers evaluated a continuous mobile authentication model using deep learning autoencoders. Their scheme utilized a cloud system to manage communication between their classification algorithm and their collected data. For evaluation, this study used motion sensor data from two public datasets intended for continuous mobile authentication: the HMOG dataset and a crowdfunded dataset with data from 20 subjects. Their deep learning classifier with autoencoders was trained and tested on these datasets. Results found that their model was flexible enough to handle different authentication contexts, with an EER of 2.2% with five layers and 15 min intervals between model rebuilds. Researchers of [42] proposed a mobile authentication scheme utilizing the motion sensor data collected from the action of turning over a mobile device.