## 3 Literature Review
### Behavioral Biometrics
#### 3.1.2 Motion Dynamics
In their dataset, 19 volunteers were instructed to turn their mobile phone over multiple times in different positions, from which accelerometer and gyroscope data were collected. Their classifier, a DNN, was trained with custom movement samples from this dataset. Results from this study found that their model produced viable metrics in both sitting and standing postures and was stable over time, with best metrics of 94-98% accuracy and 9-1% FRR and FAR. Machine learning is also a popular method of classification for motion biometrics. Studies [43, 44, 45, 46] used SVM classifier. In [43], researchers proposed a continuous authentication scheme via motion activity in different contexts. Their dataset consisted of motion sensor data from ten participants performing six different activities: walking, sitting, standing, running, and moving upstairs and downstairs. Three classifiers were tested with their authentication model: SVM, DT, and KNN. SVM achieved the highest accuracy results in all six activities between the comparison algorithms. SVM was decided to be the best for on-device user authentication with an overall average recognition accuracy of 97.95%. Researchers of [44] proposed RiskCog, a mobile authentication scheme using motion sensor data from both the mobile device and a smartwatch wearable device. Binary SVM with RBF kernel was used as a classification algorithm. Their model was tested with motion sensor data from six different public datasets. Results found that RiskCog was able to achieve higher accuracy rates than comparison studies, with 93.77% in steady conditions and 95.57% in moving conditions on dataset IV. Dataset IV consisted of data collection over 10 days from 1530 users. RiskCog was also able to resist brute force and mimicry attacks. Study [45] proposed SmartCAMPP, a continuous mobile authentication system via motion sensors. SmartCAMPP authenticates users by encrypting and preprocessing accelerometer and gyroscope data before authenticating with an ML classifier. Their model was tested on the Sherlock dataset, consisting of motion sensor data from 52 users over 500 h. Three different classifiers were tested, including SVM, RF, and LR. In baseline authentication results, SVM achieved a highest accuracy of 82.26%. SmartCAMPP's results produced an accuracy \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Algorithm** & **Accuracy (\%)** \\ \hline
[12] & DNN & 89.90 \\
[33] & CNN + OC-SVM & 97.80 \\
[34] & CNN + SVM & 96.72 \\
[38] & LSTM & 99.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of studies with HMOG dataset [12, 33, 34, 38]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Algorithm** & **EER (\%)** \\ \hline
[13] & DNN & 3.4 \\
[16] & KRR & 0.01 \\
[18] & AC-GAN & 7.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of studies with Touchalytics dataset [13, 16, 18]. of 76.85%, with a 5% reduction due to encryption. For [46], researchers analyzed behavioral biometrics to increase mobile authentication accuracy as compared to physiological. Their study analyzed a model based on accelerometer and gyroscope motion sensing. A dataset consisting of data recorded from 60 users lifting a phone to take a call was used to train and test four classifiers: SVM, KNN, MLP, and NB. Results found that while MLP had the highest average accuracy, it carried a heavy data processing footprint. SVM was claimed to be the best performing algorithm in both accuracy and efficiency with around 92% accuracy in both simple and complex scenarios. In [47, 48], researchers used RF as a classifier. For study [47], researchers proposed ADLAuth, an implicit mobile authentication scheme using motion sensor data from both the mobile phone and a wearable device. The methodology for their authentication system is shown in Figure 3. Their model used three different datasets ranging from 9 to 59 participants performing static and dynamic activities. SVM, DT, and RF classifiers were tested with the model and datasets.