## 5 Discussion  
### Adversary Model  
We assumed that an attacker penetrates the victim's terminal and installs malicious programs. These are reasonable assumptions given that malicious codes continue to emerge and increase the number of zombie PCs. In this attack situation, the attacker's level is classified into two categories: professional attacker and ignorant attacker. The professional attacker exploits C/D bit vulnerability as described in Section 2.4 to steal keyboard data. A professional attacker has a high level of knowledge of the device driver. Moreover, when the attacker uses this attack technique, it obviously steals the real keyboard data. This means that an adversary neutralizes password authentication. This attack, however, induces an overload on the system with up to 99% CPU utilization, and can be detected as a malicious code by judging according to abnormal behavior and access. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Model** & **D** & **AC** & **+**\(-\) & **P** & **+**\(-\) & **R** & **+\(-\)** & **F** & **+\(-\)** & **AU** & **+\(-\)** \\ \hline \multirow{2}{*}{KNN} & w/o & 0.877 & \multirow{2}{*}{109.1} & 0 & \multirow{2}{*}{-} & 0 & \multirow{2}{*}{-} & 0 & \multirow{2}{*}{-} & 0 & \multirow{2}{*}{-} & 0.311 & \multirow{2}{*}{295.1} \\ \cline{2-2} \cline{6-11}  & w & 0.957 & & & 0.926 & & & & & & & \\ \hline \multirow{2}{*}{Logistic Regression} & w/o & 0.879 & \multirow{2}{*}{108.6} & 0 & \multirow{2}{*}{-} & 0 & \multirow{2}{*}{-} & 0 & \multirow{2}{*}{-} & 0.418 & \multirow{2}{*}{225.5} \\ \cline{2-2} \cline{6-11}  & w & 0.955 & & & 0.972 & & & & & & & \\ \hline \multirow{2}{*}{Linear SVC} & w/o & 0.215 & \multirow{2}{*}{420.4} & 0.128 & \multirow{2}{*}{781.2} & 0.944 & \multirow{2}{*}{**21.8**} & 0.226 & \multirow{2}{*}{**150.8**} & 0.581 & \multirow{2}{*}{162.3} \\ \cline{2-2} \cline{6-11}  & w & 0.904 & & & 1 & & & & & & \\ \hline \multirow{2}{*}{Decision Tree} & w/o & 0.879 & \multirow{2}{*}{108.1} & 0 & \multirow{2}{*}{-} & 0 & \multirow{2}{*}{-} & 0 & \multirow{2}{*}{-} & 0.568 & \multirow{2}{*}{166.3} \\ \cline{2-2} \cline{6-11}  & w & 0.951 & & & 0.971 & & & & & & & \\ \hline \multirow{2}{*}{Random Forest} & w/o & 0.852 & \multirow{2}{*}{111.5} & 0.205 & \multirow{2}{*}{405.8} & 0.075 & \multirow{2}{*}{984} & 0.11 & \multirow{2}{*}{710.9} & 0.543 & \multirow{2}{*}{174.7} \\ \cline{2-2} \cline{6-11}  & w & 0.95 & & & 0.832 & & & & & & \\ \hline \multirow{2}{*}{Gradient Boosting} & w/o & 0.88 & \multirow{2}{*}{108.2} & 0.667 & \multirow{2}{*}{135.2} & 0.019 & \multirow{2}{*}{3642.1} & 0.036 & \multirow{2}{*}{2175} & 0.68 & \multirow{2}{*}{143.3} \\ \cline{2-2} \cline{6-11}  & w & 0.953 & & 0.902 & & & & & & & \\ \hline \multirow{2}{*}{SVM} & w/o & 0.877 & \multirow{2}{*}{108.4} & 0 & \multirow{2}{*}{-} & 0 & \multirow{2}{*}{-} & 0 & \multirow{2}{*}{-} & 0.477 & \multirow{2}{*}{197.4} \\ \cline{2-2} \cline{6-11}  & w & 0.951 & & & 0.971 & & & & & & & \\ \hline \multirow{2}{*}{MLP} & w/o & 0.876 & \multirow{2}{*}{108.3} & 0 & \multirow{2}{*}{-} & 0 & \multirow{2}{*}{-} & 0 & \multirow{2}{*}{-} & 0.418 & \multirow{2}{*}{226.0} \\ \cline{2-2} \cline{6-11}  & w & 0.949 & & & 0.97 & & & & & & & \\ \hline \hline \multicolumn{11}{l}{D: Data set. w/o: without Timestamp. w: with Timestamp. AC: accuracy. P: Precision. R: Recall. F: F1-score. AU: AUC. +-: Increase and decrease rates.} \\ \end{tabular}
\end{table} 
Table 7: Change rate of increase and decrease according to feature (Dataset 1). An attacker using the attack technique discussed in this paper is assumed to be an ignorant attacker. An ignorant attacker is assumed to have simple programming skills at the application level or the ability to use publicly available attack tools such as keyloggers. Therefore, as described in Section 2.4, this attacker cannot use C/D bit vulnerability to steal keyboard data, hence an adversary does not neutralize password authentication. However, if this attack technique discussed in this paper is used, the ignorant attacker can steal the keyboard data.