# Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach
## 7 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{6}{c}{**Test Set (\%)**} \\ \cline{2-7}  & & _Accuracy_ & _Precision_ & _Recall_ & _F1_ & _FNR_ \\ \hline \multirow{6}{*}{_DoS_} & _SOMK-C_ & _97.85_ & 91.21 & _100.00_ & 95.40 & _0.00_ \\  & _CI (95\%)_ & _97.22–98.49_ & _89.97–92.45_ & _100.00–100.00_ & 94.48–96.32_ & _0.00–0.00_ \\ \cline{2-7}  & SOMK-D & 100.00 & 100.00 & 100.00 & 100.00 & 0.00 \\  & CI (95\%) & 100.00–100.00 & 100.00–100.00 & 100.00–100.00 & 100.00–100.00 & 0.00–0.00 \\ \hline \multirow{6}{*}{_Spoofing_ _ear_} & _SOMK-C_ & _95.50_ & _79.36_ & _100.00_ & _88.49_ & _0.00_ \\  & _CI (95\%)_ & _94.59–96.41_ & _77.58–81.13_ & _100.00–100.00_ & 87.09–89.89_ & _0.00–0.00_ \\ \cline{2-7}  & SOMK-D & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 0.00 \\  & CI (95\%) & 100.00–100.00 & 100.00–100.00 & 100.00–100.00 & 100.00–100.00 & 0.00–0.00 \\ \hline \multirow{6}{*}{_Spoofing_ _RPM_} & _SOMK-C_ & 95.75 & _80.41_ & _100.00_ & _89.14_ & _0.00_ \\  & _CI (95\%)_ & 94.87–96.63_ & _78.68–82.15_ & _100.00–100.00_ & 87.78–90.51 & _0.00–0.00_ \\ \cline{2-7}  & SOMK-D & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 0.00 \\  & CI (95\%) & 100.00–100.00 & 100.00–100.00 & 100.00–100.00 & 100.00–100.00 & 0.00–0.00 \\ \hline \multirow{6}{*}{_Fuzzy_} & _SOMK-C_ & **72.55** & _0.00_ & _0.00_ & - & _100.00_ \\  & _CI (95\%)_ & _70.59–74.51_ & _0.00–0.00_ & _0.00–0.00_ & - & _100.00–100.00_ \\ \cline{2-7}  & SOMK-D & **99.40** & **98.92** & **97.87** & **98.39** & **2.13** \\ \cline{2-7}  & CI (95\%) & 99.06–99.74 & 98.47–99.37 & 97.23–98.50 & 97.84–98.94 & 1.50–2.77 \\ \hline \multirow{6}{*}{_Mixed_} & _SOMK-C_ & _87.01_ & _61.05_ & _38.41_ & _47.15_ & _61.59_ \\  & _CI (95\%)_ & _85.53–88.48_ & _58.92–63.19_ & _36.28–40.54_ & 44.97–49.34 & _59.46–63.72_ \\ \cline{2-7}  & SOMK-D & 97.60 & 86.29 & 100.00 & 92.64 & 0.00 \\ \cline{1-1}  & CI (95\%) & 96.93–98.27 & 84.78–87.79 & 100.00–100.00 & 91.49–93.78 & 0.00–0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation metrics for test sets. the drive gear_, and _spoofing the RPM_ gauge datasets. Moreover, it significantly reduced the false negative rate, which has great importance in attack detection for in-vehicle CAN buses in order to ensure the safety of the vehicle. Due to the availability of the open source data, this work only tested the proposed IDS on a limited number of attacks. Future works should involve investigating the development of the Kohonen SOM network as an anomaly detector by extending it to other types of attacks in a CAN bus. Conceptualization, D.C.; data curation, V.S.B., A.N. and M.S. ; formal analysis, A.N. ; investigation, V.S.B. and D.C.; methodology, D.C. and A.N. ; project administration, D.C.; software, V.S.B., A.N., and M.S. ; supervision, M.S. ; validation, V.S.B. and D.C.; visualization, V.S.B. and A.N. ; writing--original draft, V.S.B. and A.N. ; writing--review and editing, D.C. and M.S. All authors have read and agreed to the published version of the manuscript. This research was funded by \"HEVOLUS+\" in the context of \"Aiuti ai Programmi di Investmento promossi da Piccolle Imprese da concedere ai sensi dell'art. 27 del Regolamento Regionale per gli aiuti in esenzione n.17 del 30/09/2014, TITOLO II capo 2 del Regolamento Generale\"; \"AURIGA-PBM\" in the context of \"Aiuti ai Programmi di Investmento promossi da Medicie Imprese da concedere ai sensi dell'art. 26 del Regolamento Regionale per gli aiuti in esenzione n.17 del 30/09/2014, TITOLO II capo 2 del Regolamento Generale\"; \"Auriga2020\" in the context of \"Aiuti ai Programmi di Investmento promossi da Medicie Imprese da concedere ai sensi dell'art. 26 del Regolamento Regionale per gli aiuti in esenzione n.17 del 30/09/2014, TITOLO II capo 2 del Regolamento Generale\". The authors declare no conflict of interest.