# [PERSON] SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks
## 6 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Type**} & \multicolumn{3}{c}{**Min**} & \multicolumn{3}{c}{**Median**} & \multicolumn{3}{c}{**Mean**} & \multicolumn{3}{c}{**Max**} & \multicolumn{3}{c}{**Standard Deviation**} \\ \cline{3-14}  & & \(\mathbf{2\times 2}\) & \(\mathbf{4\times 4}\) & \(\mathbf{8\times 8}\) & \(\mathbf{2\times 2}\) & \(\mathbf{4\times 4}\) & \(\mathbf{8\times 8}\) & \(\mathbf{2\times 2}\) & \(\mathbf{4\times 4}\) & \(\mathbf{8\times 8}\) & \(\mathbf{2\times 2}\) & \(\mathbf{4\times 4}\) & \(\mathbf{8\times 8}\) & \(\mathbf{2\times 2}\) & \(\mathbf{4\times 4}\) & \(\mathbf{8\times 8}\) \\ \hline \multirow{2}{*}{_DoS_} & **Attack** & 0.01 & 0.00 & 0.00 & 0.01 & 0.00 & 0.00 & 0.01 & 0.00 & 0.00 & 0.01 & 0.00 & 0.01 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\  & **Normal** & 0.05 & 0.00 & 0.00 & 0.05 & 0.98 & 0.51 & 0.05 & 0.85 & 0.51 & 0.07 & 1.51 & 1.24 & 0.00 & 0.24 & 0.21 \\ \hline \multirow{2}{*}{_Spoofing_} & **Attack** & 0.02 & 0.00 & 0.00 & 0.02 & 0.00 & 0.00 & 0.02 & 0.00 & 0.00 & 0.02 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\  & **Normal** & 0.05 & 0.01 & 0.00 & 0.05 & 0.83 & 0.51 & 0.05 & 0.76 & 0.57 & 0.07 & 1.52 & 1.58 & 0.00 & 0.24 & 0.23 \\ \hline \multirow{2}{*}{_Spoofing_} & **Attack** & 0.02 & 0.00 & 0.00 & 0.02 & 0.00 & 0.00 & 0.02 & 0.00 & 0.00 & 0.02 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\  & **Normal** & 0.08 & 0.00 & 0.00 & 0.08 & 0.90 & 0.51 & 0.08 & 0.81 & 0.52 & 0.10 & 1.43 & 0.82 & 0.00 & 0.24 & 0.22 \\ \hline \multirow{2}{*}{_Fuzzy_} & **Attack** & 0.07 & 0.67 & 0.00 & 0.07 & 1.51 & 0.96 & 0.07 & 1.51 & 0.90 & 0.08 & 2.13 & 1.80 & 0.00 & 0.06 & 0.07 \\  & **Normal** & 0.05 & 0.00 & 0.00 & 0.05 & 1.32 & 1.93 & 0.05 & 1.22 & 1.93 & 0.06 & 1.61 & 2.39 & 0.00 & 0.33 & 0.43 \\ \hline \multirow{2}{*}{_Mixed_} & **Attack** & 0.06 & 0.00 & 0.00 & 0.06 & 0.53 & 0.00 & 0.06 & 0.43 & 0.24 & 0.08 & 1.02 & 0.96 & 0.01 & 0.30 & 0.37 \\  & **Normal** & 0.05 & 0.01 & 0.00 & 0.05 & 0.76 & 0.45 & 0.05 & 0.65 & 0.50 & 0.07 & 1.20 & 0.89 & 0.00 & 0.19 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Distances between the input vectors and the winning neurons metrics in the XYF network. Figure 10: Distances between the input vectors and the winning neurons for the XYF network on the \(8\times 8\) neurons map. [PERSON] et al. proposed an anomaly-based IDS using a supervised deep convolutional neural network (DCNN) [30]. Experimental results showed that their approach reached a higher performance compared to other machine learning algorithms, in particular, long short-term memory, artificial neural network, support vector machine, k-nearest neighbors, naive Bayes, and decision trees. [PERSON] et al. suggested a specification-based IDS using a response time analysis of the CAN bus. They restricted the analysis only checking periodic and sporadic messages [38]. [PERSON] et al. proposed an anomaly-based IDS using a deep-learning model, in particular, generative adversarial nets (GAN) [27]. Results (Table 8) show how the XYF-K algorithm presents superior performance compared to all the other methods for all the datasets. Only the DCNN proposed by [30] reveals a slightly better accuracy for the fuzzy dataset, in particular, the authors claim an accuracy of 99.82% while in our study it is equal to 99.75%. Overall, we