## 3 Results
### Evaluations On The Automatically Generated Dataset
#### 3.1.2 Noisy Dataset  
By only inserting random irrelevant actions into each pattern session of the baseline dataset to generate a noisy dataset, the number of noise action inserts into each of the pattern sessions can be specified. We inserted 1-10 noise actions (10 noise levels) into the random positions of each pattern session to observe the prediction errors which are compared to the baseline dataset. There were 10 tests for each noise level, for each test, the data set was regenerated to ensure sufficient randomness. The system made 10 predictions based on different inputs and the prediction errors were counted. The average influences of each candidate action were calculated and the top n actions with the highest average influences were selected as the prediction results which were compared with the predictions shown in Table 3.  
A part of the generated noisy dataset is shown in Figure 4, of which each row contains an intrusion session. The actions that start with \"b\" are the noise actions, and all the actions are divided by a comma.  
\begin{table}
\begin{tabular}{c c c} \hline
**Pattern Sessions** & **Appear Times 1** \\ \hline a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14 & 5 \\ a15, a16, a17, a18, a19, a20 & 5 \\ a21, a22, a23, a24, a25, a26, a27, a28, a29 & 5 \\ a30, a31, a32, a33, a34, a35, a36, a37, a38, a39 & 4 \\ a40, a41, a42, a43, a44, a45, a46, a47, a48, a49 & 5 \\ a50, a51, a52, a53, a54, a55 & 4 \\ a56, a57, a58, a59, a60, a61, a62, a63, a64 & 4 \\ a65, a66, a67, a68, a69, a70, a71, a72 & 5 \\ a73, a74, a75, a76 & 3 \\ a77, a78, a79, a80, a81, a82, a83, a84, a85, a86, a87, a88, a89, a90 & 5 \\ \hline \end{tabular}
\end{table}
Table 2: Generated pattern sessions.  
\begin{table}
\begin{tabular}{c c} \hline
**Inputs** & **TopPredicted Actions (Average Influence Factor)** \\ \hline a1 & a2(0.020), a3(0.013), a4(0.010), a5(0.008), a6(0.007) \\ a6 & a7(0.020), a8(0.013), a9(0.010), a10(0.008), a11(0.007) \\ a16 & a17(0.027), a18(0.018), a19(0.013), a20(0.011) \\ a24 & a25(0.033), a26(0.022), a27(0.017), a28(0.013), a29(0.011) \\ a33 & a34(0.027), a35(0.018), a36(0.013), a37(0.011), a38(0.009) \\ a40 & a41(0.032), a42(0.027), a43(0.023), a44(0.020) \\ a52 & a53(0.027), a54(0.018), a55(0.013) \\ a61 & a62(0.033), a63(0.022), a64(0.017) \\ a68 & a69(0.020), a70(0.013), a71(0.010), a72(0.008) \\ a83 & a84(0.027), a85(0.018), a86(0.013), a87(0.011), a88(0.009) \\ \hline \end{tabular}
\end{table}
Table 3: Prediction results based on the baseline dataset.  
The accuracy of the analysis based on the noisy dataset is shown in Table 4.  
The evaluation results are shown in Figure 5:  
As shown in Figure 5, the average accuracy of the prediction decreases with the increasing of the noise level. When inserting 10 noise actions into each pattern sessions, the prediction accuracy stays above 93.7%. The average accuracy of predictions based on the input actions is 96.3%. In a word, the proposed algorithm can learn the patterns from the noisy sequences with a higher accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Inputs** & **Prediction Errors** & **Prediction Accuracy 1** \\ \hline a1 & (0,0,0,0,3,3,4,0,3,7) & 98.5\% \\ a6 & (0,0,0,1,2,1,0,0,1,2) & 99\% \\ a16 & (0,0,4,0,3,1,7,6,10,0) & 92.3\% \\ a24 & (1,0,0,0,1,2,2,2,5,10) & 95.4\% \\ a33 & (0,0,4,2,1,7,3,12,2,8) & 93.3\% \\ a40 & (1,0,2,0,1,4,4,2,8,0) & 97.6\% \\ a52 & (0,0,1,0,1,2,4,2,2,5) & 94.3\% \\ a61 & (0,0,0,1,0,1,0,2,0,1) & 98.3\% \\ a68 & (0,0,1,0,0,0,5,4,6,5) & 94.8\% \\ a83 & (0,0,0,0,0,1,0,2,0,1) & 99.4\% \\ \hline \multicolumn{3}{c}{\({}^{1}\)_accuracy(action) = \((1-\frac{\text{Ractions through weakly predicted}}{\text{Ractions should be correctly predicted}})\times 100\%\)._} \\ \end{tabular}
\end{table}
Table 4: Prediction results based on the noisy dataset.  
Figure 5: The prediction accuracy based on different noisy levels.