## 3 Literature Review
### Behavioral Biometrics
#### 3.1.1 Touch Dynamics
To train their models, a dataset was collected from 20 participants tasked with performing various touch input such as single touch, multitouch, movement, and pattern-lock. PSO + RBFN was found to have the best performance with 2.0% FAR, 1.9% FRR, and 1.95% AER. In study [18], researchers proposed a touch stroke authentication model based on auxiliary classifier-generative adversarial network (AC-GAN). Their AC-GAN model consisted of a generator, discriminator, and a composite model with DNN structure in both the generator and discriminator. For model evaluation and training, the Touchalytics dataset was used. Authentication evaluation was performed by randomly selecting 10 subjects from the dataset to provide genuine user data, while the AC-GAN generated a balanced amount of synthetic imposter data to test against. Their model achieved EER, ranging from 2% to 11%, between subjects with a median of 7%, providing comparable performance to other touch stroke authentication systems. Researchers in study [19] proposed a continuous touch authentication system via a deep autoencoder with SoftMax regression (DAE-SR). For their model, butterfly optimization algorithm (BOA) was used to extract features before sending the input to DAE-SR for classification. Ten subjects' data from the HMOG dataset were used for model training and evaluation, where touch interaction and motion sensor data in controlled walking and sitting scenarios were collected. Their proposed model was able to outperform comparative classifiers SVM and KRR in both scenarios, with a best accuracy of 0.970 in sitting and a best EER of 0.030 in sitting. Many other studies working with touch-based mobile authentication chose machine learning classifiers to make their authentication decisions. Studies [20, 21, 22, 23, 24] implemented SVM classifier in their models. In [20], researchers created an android app unlocking mechanism that utilized behavioral biometrics and ML for enhanced application security. Their application used pattern unlock to collect touch biometrics data and was evaluated via one class SVM (OC-SVM) and K-means ML algorithms. Their application and methodology were evaluated via 64 users and resulted in 93.7% true positive authorizations and 86% true negative authorizations. Touch biometrics data collected via pattern lock were also tested with OC-SVM in [21]. Researchers in this study aimed to address security issues with pattern lock via context awareness and fine-grained feature detection. In their model, motion sensor data collected from 77 users performing pattern lock input were used to identify posture context. Segmentation of pattern lock features via polylines enabled an improved accuracy and fine-grained detection. When this model was tested with different classifiers, OC-SVM outperformed. Their model was able to effectively improve pattern lock authentication with an EER of 5.1139% with context awareness and 98.96% accuracy with S pattern. In study [22], researchers tested SVM and Gaussian mixture model (GMM) with swiping biometric data from four different benchmark datasets: Serwadda, Frank, Antal, and the UMDAA-02 database. While SVM performed best with stable users, GMM performed best with unstable users. When fusing both SVM and GMM, the model produced better results overall in all sessions and datasets. Researchers found that the best performing operations were horizontal swipe gestures, and that landscape mode provided more stability. Their model with SVM and GMM fusion in intra-session scenarios was able to achieve 3-6% EER on the Serwadda dataset and around 3% EER with the Frank dataset. For [23], researchers [PERSON] et al. analyzed touch behavioral authentication when used with email application usage data. For their model, they collected touch action data from sixty participants in three scenarios: email usage, social networking usage and free usage. Five classifiers were compared on their performance while authenticating the collected data.