## 3 Literature Review
### Behavioral Biometrics
#### 3.1.1 Touch Dynamics
A best average error rate (AER) of 2.9% was achieved by SVM classifier with email usage data. Researchers of [24] proposed a mobile authentication model via touch operations. Data from 10 subjects were collected while performing tasks such as tapping, swiping, and rotating. SVM classifier achieved better precision in single operations, but achieved a better accuracy when combining operations. The best accuracy for their model was 97.7% when tested with double tap and rotation data. Studies [25, 26, 27] chose random forest as a classifier. In [25], researchers investigated the use of touch swipe and micromovement biometric data for authentication. For data collection, 40 subjects were tasked with answering a questionnaire using a slider. The data were divided into swipes and then extracted for features. For evaluation, many one-class and two-class classifiers were tested for user authentication. Results found that constrained horizontal swipes were effective with both one and two-class classifiers, and that accelerometer data improved performance. Random forest provided a best EER of (0.002 \(\pm\) 0.000) with five swipes and eleven features. Researchers of [26] proposed a risk-based continuous authentication scheme with touch and motion data collected from unrestricted pin entry. Data were collected from 95 subjects in three scenarios: sitting, standing, and walking. These data were used to train a naive Bayes (NB) classifier, a neural network (NN), and a random forest classifier. Results found random forest to have the best and most consistent results in all scenarios due to its ability to reduce variances and tune to overfitting. With 15 samples, RF achieved a highest TAR of 91.79%. For [27], researchers proposed an authentication scheme that identified hand motions (HM) and hold posture (HP) via touch and motion sensor interaction data for user verification. The study aimed to create a model that could authenticate in both dynamic and static scenes without constrained environments and interactions. For data collection, ten students posed as smartphone users and four researchers posed as imposters. KNN, SVM, and RF were trained and tested with the collected data, and all achieved above 94% accuracy, demonstrating the effectiveness of HMHP features. RF had the highest performing metrics with 0.99 AUC and >0.1% EER. Researchers of [28, 29, 30] chose other ML classifiers for their models. In study [28], a continuous touch-dynamics authentication method was studied. Researchers used the Touchlogger application to collect background touch data during phone usage. For data collection, 14 participants collected general usage data over the course of two weeks, consisting mainly of single touch actions. RF, KNN, gradient boosting classifier (GBC), and linear SVM (L-SVM) classifiers were evaluated with these data. While RF and GBC had similar accuracy, GBC was chosen as the optimal classifier due to a faster performance with an average accuracy under the ROC curve (AUC) of 0.9692 and a learning time of 117 s. For [29], researchers attempted to create a privacy-preserving global continuous authentication model using touch dynamics. To achieve this, base features would be extracted from initial user sessions. Behavioral embedding was then performed, in which current session data would be transformed and embedded into past datasets. The global model would be trained on this embedded dataset. A total of 9 k subjects were tasked with data collection. Their method was tested on three algorithms: XGBost (XGB), linear GBC, and DNN. The best results were achieved with L-GBC with an AUC of 0.913-0.921%, and an EER of 15.3-15.9%. Gradient boosting methods both outperformed the DNN. When compared with state-of-the-art (SOTA) techniques with OC-SVM and isolation forest (IF), their methods significantly outperformed. A touchscreen authentication model utilizing biometric information from both a smartphone and a smartwatch was proposed in [30]. For their model, a user performs a touch swiping task on the smartphone while wearing a smartwatch.