## 3 Literature Review
### Behavioral Biometrics
#### 3.1.2 Motion Dynamics  
Deep learning algorithms are a popular choice for authentication in many recent studies involving motion dynamics. In studies [31, 32, 33, 34, 35, 36], CNN was used to accomplish authentication. Hybrid models involving CNN feature extraction and SVM classification were noted frequently. Researchers of [31] aimed to combat DL vulnerability by testing a CNN model against adversarial attacks. Their authentication model was based on gesture authentication via the usage of time-series triaxial accelerometer data. To build their dataset, 16 users held a smartphone and traced a gesture in the air. A one-dimensional-CNN (1D-CNN) and an SVM were trained and tested on this data for authentication. Adversarial samples generated by deep convolutional GAN (DC-GAN) were used to evaluate CNN's robustness against adversarial attacks. Their CNN model slightly surpassed their SVM model with an accuracy range of 90% to 94% after hyperparameter fine-tuning was performed. Their CNN model was also able to resist adversarial attacks with a 90% accuracy facing poisoning attacks and almost completely overcoming evasion attacks. In study [32], researchers aimed to enhance the representational power of features and decrease the computational cost of continuous authentication models. Their model, SCANet, utilized a hybrid model involving a two-stream CNN based on depthwise separable convolution to extract features with high discriminability and an OC-SVM for classification. Their dataset included motion sensor data collected from 100 volunteers who performed tasks such as reading, writing, and navigation. When compared with other models, their CNN was able to achieve best accuracies of 87.53% and 90.04% in 2 s and 5 s time windows, respectively. A similar hybrid model and dataset were used in study [33]. Researchers proposed a continuous mobile authentication scheme using motion patters. A Siamese CNN performed feature extraction and OC-SVM performed classification. The HMOG dataset was used to evaluate their model. Their scheme produced a highest authentication accuracy of 97.8% with a sampling frequency of 25 Hz and a window size of 1 s in an analysis of all scenarios combined. Researchers of [34] also used a CNN-SVM hybrid model. Their approach generated grayscale images based on three-axis motion sensor signals from a single tap touch action and used these images to discriminate users. Touch data in the sitting position from the HMOG dataset were utilized for this study. A six-layer CNN model performed feature selection and an SVM was trained for classification. Their model was compared to LSTM feature and handcrafted feature models. Overall, their hybrid model significantly outperformed, with 96.72% accuracy. For study [35], a hybrid model for real-world mobile authentication was proposed. This model was aimed to combat issues with noise-in-motion sensor data when collected in real-world settings. A four-layer CNN was chosen to perform feature extraction and an SVM was used for classification. This CNN + SVM architecture is illustrated in Figure 2. A variational mode decomposition function (VMD) was used for data de-noising and signal enhancement. For their dataset, 1516 participants collected unlabeled and unsupervised motion sensor data over the course of one week. In comparison to other models using only ML, their CNN + SVM model outperformed with 95.01% accuracy. In [36], researchers proposed DeFrisusion, a continuous mobile authentication scheme using motion sensor data. Their dataset consisted of time-domain accelerometer and gyroscope data collected from 100 users tasked to perform three common mobile phone tasks. These data were then converted into frequency data, from which a CNN extracts features and then fuses them to train an OC-SVM for classification. Results from this study found that when compared to OC-SVM, KNN, RF, and DT features, their CNN-based feature model performed the best with the SVM classifier.