# Continuous Mobile User Authentication Using Combined Biometric Traits
## 3 Related Research
### Studies Relying on Standard Classifiers  
The following subsection discusses continuous mobile user authentication systems relying on standard classifiers like SVM, k-NN, Decision Trees, Random Forests, or Naive Bayes. All of the papers were capturing their own dataset and extracted the features used for training the classifier from it. [PERSON] et al. [26] proposed a framework for classification in 2013 which researches the applicability of touch-based authentication based on human behavioral biometrics. The study researches usage scenarios for a continuous authentication mechanism based on a set of 30 features extracted from raw touchscreen input. For the research, the necessary data was acquired from 41 different participants. With the used classification methods an EER in the range of 0% and 4% was achieved. This value is dependent on the previously mentioned different usage scenarios of the system. The research suggests that the proposed techniques are applicable for different scenarios, utilizing authentication based on natural user navigation [26]. The authentication process is split up into two different phases, the enrollment phase, and the authentication phase. In the enrollment phase, the touch input is captured as soon as a trigger action is detected. The two gestures, sliding horizontally and sliding vertically were considered as trigger actions. As classifiers, k-NN and a SVM utilizing an Radial Basis Function (RBF) kernel were chosen. Since the utilized classifiers rank every stroke individually, the authentication result is highly volatile. To circumvent this behavior, strokes are bundled and then classified together. The SVM classifier performs better in every scenario than the k-NN classifier, where all scenarios achieve a median EER of 0% up to 4%. For short-term authentication, the median EER is 0%, but some outliers can reach a value of 10%. Inter-session authentication provides a median EER of 2% up to 3% and inter-week authentication achieves 0% to 4%. The study found that touchscreen interactions are feasible to distinguish different users. As proposed in 2012 by [PERSON] et al. [1] the FAST (Fingergestures Authentication System using Touchscreen) system introduces a touchscreen-based system which makes user authentication after a regular login possible. In addition to the touchscreen data acquired from capable mobile devices, the solution also relys on a sensor glove to achieve continuous user authentication after the initial login phase. The system claims an FAR of 4.66% and an FRR of 0.13% with test data of 40 participants [1]. It should be noted that while a relatively high value of False Rejection Rate (FRR) typically leads to a general decrease in user acceptance, increasing False Acceptance Rate (FAR) values tend to be extremely dangerous since these enable bogus or even malignant authentication attempts to be successful. For model training, the three classification algorithms Bayes Net, Random Forest, and Decision Trees have been used. The utilized touch inputs on the mobile devices were characterized as either touch gestures, virtual typing, or touch-based drawing, and depending on the category features were extracted from the dataset. This dataset contains 53 features for each touch gesture the user makes. Six different gestures are considered and evaluated by the system, whereas every gesture is split up into three different states, the beginning of a touch event followed by a longer main motion and finally the ending of the event. Additionally, to the data obtained from the touch input, the system is utilizing multiple ITG3200/ADXL345 6 DOF ([[https://www.sparkfun.com/products/retired/10121](https://www.sparkfun.com/products/retired/10121)]([https://www.sparkfun.com/products/retired/10121](https://www.sparkfun.com/products/retired/10121)), accessed 4 December 2021) sensors. Those are combined in a sensor glove to obtain 36 more features where each unit provides X, Y, and Z angular rate, pitch, roll, and yaw.