## 3 Literature Review
### Behavioral Biometrics
#### 3.1.3 Keystroke Dynamics  
Many keystroke dynamics-based authentication methods have used deep learning algorithms to make their authentication decisions. Studies such as [49; 50; 51; 52; 53; 54] used RNN-based deep architectures. In [49], researchers tested many types of touch modalities, including keystroke on their effectiveness with various motion sensors. They used HuMldb, a large-scale database including over 600 subjects. For the keystroke data, timestamp and keypress data were recorded in a fixed text scenario. Individual LSTM-RNN authentication models were trained with each modality and then fused at score level. Compared to other touch-based modalities, keystroke outperformed with a best EER of 4.62% when touch data were fused with accelerometer, magnetometer, and gravity sensor data. It was also found that the fusion of modalities led to a better performance. Similar results were found in [50], where researchers also used an LSTM-RNN model with Siamese architecture. Keystroke touch and temporal data along with motion and GPS sensor data were collected continuously in a free text scenario from a dataset of 37 subjects. Each modality was individually evaluated on their performance and fused. Their Siamese LSTM network was trained with deep temporal features of the various modality combinations. Results found that a highest TAR of 99.98% was achieved with all eight sensor modalities fused. Performance saturated after five modalities were used, with a 99.80% and 97.15% TAR at 0.1% FAR with 3 s and 5 s, respectively. In [51], researchers tested an RNN and other algorithms for mobile identification via keystroke and touch dynamics. The dataset used included keystroke and swipe data from 31 subjects collected in a supervised environment. Multimodal features were fused at score level and then used to train with various algorithms. RNN was able to achieve the best classification results with unimodal classification as well as multimodal classification. With all features fused late using weighted product rule, RNN achieved slightly better results than CNN with 95.29% accuracy and 1.78% EER. In [52], researchers proposed TypeNet, where an LSTM-RNN architecture was used for keystroke authentication. In the mobile scenario, free-text keystroke temporal data collected from 26,000 participants in the [PERSON] et al. dataset was used for training an RNN with three loss functions. Their model was able to achieve 9.2% EER in mobile authentication with a triplet loss function when balancing enrollment data and performance. In [53], weighted results from and RNN and a SVM were used to authenticate keystroke data. A small dataset consisting of motion sensor, touchscreen, and temporal data from 10 users was used to train the models. It was found that a combination of motion sensor and touchscreen data had the best results, with 93.9% accuracy. Researchers of [54] proposed a touch database, BenchPassDB, which was evaluated with an LSTM-RNN classification model. BenchPassDB consisted of touch and motion sensor data from eight different touch interaction tasks performed by 81 subjects. Between their eight different touch tasks, keystroke interaction had the best authentication performance with 68.72% AUC. Their results found that discriminative ability was enhanced by a fusion of modalities. Other methods [55; 56; 57; 58] used non-RNN deep learning architectures. In [55], a CNN algorithm with transformer architecture was used for classification. The Aalto mobile keystroke database was used for model training and experimentation, consisting of free-text keystroke data from 260k subjects. Five timestamp-based features were extracted from each keypress. Results from their model were compared with the TypeNet [52] RNN model on the Aalto database. Their deep transformer architecture outperformed TypeNet on 10 enrollment sessions with 3.15% EER compared to 8.00% EER. For [56], a DNN classification model was created, consisting of layers of restricted Boltzmann machines (RBMs).