## 7 Results and Discussion
### User Identification  
As described by [PERSON] et al. [2], to identify a user, for each activity, data (using one or more sensors) for all users is combined. There are 51 classes in the identification dataset, and each subject represents a distinct one. In this situation, the training set must include data from all of the participants, and the training and test sets' subjects should overlap. The experiment was executed for various sensor combinations with three different algorithms, but, as has already been established by [PERSON] et al. [2], the sensors from the phone perform better than that from the watch and the accelerometer outperforms the gyrosensor; the rest of this work talks about the results from the phone accelerometer, all accelerometers (phone-accelerometer+watch-accelerometer) and all sensors combined. Out of the three machine learning algorithms used, different algorithms perform better for each activity, but for the average performance overall activities, Random Forest performs the best, as can be inferred from Table 2.  
The activities which perform the best are automatically safer to be used for user verification. Looking at non-hand oriented activities, walking and jogging show the best results for all three algorithms, with 97.6 and 96.9 being the highest accuracy that is achieved with RandomForest. Walking and jogging, which are means of gait-based biometrics, have also proven to perform well in the literature [31; 32]. In the instance of considering hand-oriented activities, in the general category, typing and clapping produce the best accuracy, with the highest being 97.8 and 98.8 using the RandomForest model. These activities can be recorded using very short periods and thus make a handy case for behavioural biometrics. The hand-oriented eating activities that perform the best are drinking and eating a sandwich, with their best case accuracy being 98.8 and 96.4. If we compare the overall results from all activities, clapping and drinking are the best performers. They work better than gait-based activities which have been thoroughly studied for behavioural biometrics. This aspect of our results presents a motivation for implementing motion-based biometrics using hand-oriented activities. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Random**} & \multicolumn{3}{c}{**XG**} \\  & \multicolumn{3}{c}{**Forest**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**Boost**} \\ \hline \multirow{2}{*}{**Act**} & **Ph** & **All** & **All** & **Ph** & **All** & **All** & **Ph** & **All** & **All** \\  & **Accel** & **Accel** & **Sensor** & **Accel** & **Accel** & **Sensor** & **Accel** & **Accel** & **Sensor** \\ \hline A & 97.6 & 90.5 & 85.3 & 98.3 & 79.4 & 76.3 & 94.6 & 89.1 & 82.5 \\ B & 96.9 & 93.07 & 86.2 & 96.2 & 75.9 & 74.4 & 94.8 & 86.6 & 82.2 \\ C & 92.3 & 78.6 & 67.9 & 91.9 & 63.7 & 55.8 & 89.1 & 77.6 & 59.8 \\ D & 95.2 & 83.8 & 70.3 & 92.4 & 76 & 57.5 & 93 & 87.9 & 66.4 \\ E & 94.1 & 86.4 & 58.7 & 90.6 & 77.8 & 50 & 91.5 & 84.2 & 57.5 \\ F & 97.8 & 93.3 & 78.4 & 94.5 & 87.5 & 62.8 & 96.2 & 88.7 & 73.5 \\ G & 94.6 & 88.2 & 74.6 & 93.7 & 84.2 & 62.3 & 93.1 & 84.4 & 71.4 \\ H & 96 & 90.5 & 74.8 & 97.2 & 86.5 & 60.6 & 96.8 & 87.4 & 69.1 \\ I & 96 & 82.8 & 66.1 & 94 & 76.4 & 55.2 & 97 & 83.6 & 65.4 \\ J & 96.2 & 83.1 & 68.8 & 96.6 & 77.1 & 54.1 & 95.2 & 83.5 & 66.2 \\ K & 98.8 & 84.9 & 68.2 & 97 & 82.1 & 54.2 & 92.3 & 83.3 & 66.3 \\ L & 96.4 & 8 Although all these accuracies are recorded in the event of benign test samples, Figure 5 shows how this model can be fooled into misclassifying users using adversarial data. For our experiment, the adversarial data was created using the Zero Order Optimisation (ZOO) attack. This attack was chosen because it requires no model information and works on tabular as well as time-series data. Figure 5 depicts the difference in the model's misclassification error when benign and adversarial data are used.