## 3 Literature Review
### Behavioral Biometrics
#### 3.1.3 Keystroke Dynamics
The Stanford TapDynamics dataset was used for training and testing, consisting of tapping timestamp and accelerometer sensor data in a fixed PIN scenario. The results of the DNN model were compared with an SVM algorithm. Their model outperformed the SVM with a best EER of 2.8% on all 35 considered features. In [57], Kcollector was presented, using a multiview bagging with DL structures for classification. The DL structures used for the model consisted of gated recurrent unit multiview bagging RNNs (GRU-BRNN). Keystroke and accelerometer sensor data were collected from 26 subjects in a free-text scenario formodel training and experimentation. Compared to shallow machine learning approaches, their model outperformed with a best EER of 8.94% and 94.07% accuracy. In study [58], researchers evaluated a small dataset of five subjects with MLP for keystroke-enhanced PIN authentication. When compared with three other classifiers (sequential minimal optimization (SMO), naive Bayes, J-48), MLP performed the best with all 40 pressure, size, and time features, achieving a 5.43% EER. Some machine learning methods also achieved promising results with keystroke-authentication and were primarily tested with fixed keystroke data [59; 60; 61; 62]. In [59; 60], random forest was chosen as a classifier. Researchers of [59] used keystroke touch pressure and time features to enhance PIN authentication. [PERSON] et al. 's 42 subject dataset consisting of fixed typing data was used to train a random forest classifier. Results found that random forest performed best while combining pressure and timing features with an EER of 2.3%. In [60], researchers used the same dataset as [59] to evaluate random forest. In this study, finger area was used as a feature, along with timing and pressure features, adding up to 71 features tested. Five statistical methods were then used on the dataset to produce 19 more features. Along with random forest, four other classifiers were evaluated with the dataset. Random forest outperformed the other classifier with 94.26% accuracy, using all 90 features. For study [61], a multiclass SVM was evaluated for a fixed-text keystroke authentication scenario. Researchers collected data in a fixed-text scenario from 94 subjects. Thirty-six optimal features involving touch pressure, size, and coordinates features were selected via maximum relevance minimum redundancy (mRMR) wrapping. Linear SVM and RBF kernel SVM were evaluated on these features. SVM with RBF kernel has the best results, with 97.4% accuracy due to its generalization power. In [62], researchers used PCA (principal component analysis) with keystroke data to enhance PIN authentication. Motion sensor data from accelerometer, gyroscope, and others were collected from 12 subjects while entering a PIN to form their dataset. A graphic visualization of each subject's data using PCA is shown in Figure 5. For evaluation, PCA was compared with Kernel-PCA (K-PCA), OC-SVM, and local outlier factor (LOF). PCA achieved the best performance with an EER of 5% with four-digit pins and 4% with six-digit pins. PCA also achieved the lowest processing time.