# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 6 Conclusions  
In this study, an RFEI method that targets the physical layers of FHSS networks was proposed with the objective of directly identifying emitter IDs from received FH signals. An analog SF extraction process, SF spectrogram features, a DIN-based classifier for emitter classification, and an outlier detector algorithm for attacker detection were proposed and applied to the target hop signals. In addition, the ensemble approach that \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Emitter (\%)**} \\ \cline{3-4}  & & **Learned Classes** & **Outlier Classes** \\ \hline Actual emitter (\%) & Learned classes & 96.6 & 3.4 \\  & Outlier classes & 4.4 & 95.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Averaged confusion matrix of the outlier detectors based on the proposed method. Figure 15: Receiver operating characteristic (ROC) curves. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Emitter (\%)**} \\ \cline{3-4}  & & **Learned Classes** & **Outlier Classes** \\ \hline Actual emitter (\%) & Learned classes & 96.8 & 3.2 \\  & Outlier classes & 11.0 & 89.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Averaged confusion matrix of the outlier detectors based on baseline 3.  
utilized multimodality SFs was evaluated for robust classification. The results showed that the SF spectrogram extracted from the received FH signal can be effectively analyzed using the DIN-based classifier, and the classification accuracy was improved by at least 1.00% compared with those of other baselines. In addition, the multimodal SF ensemble approach, that is, the use of RT, FT, and SS, achieved the best results with a classification accuracy of 97.0% for the seven real FHSS emitters. In addition, the inception block-based approach was more effective than the residual block-based approach owing to its filtering ability at different receptive field sizes. From the analysis of the GCAM for each FH emitter, we found that the classifier model can train the region wherein the differences in the SFs can be maximized. In addition, the outlier detection performance of the proposed method was evaluated. We found that the output characteristics of the outliers differed from those of the training samples, and this property can be used by the detector to identify attacker signals with an AUROC of 0.99. These results support that the proposed RFEI method can identify emitter IDs of the FH signals emitted by authenticated users and can detect the existence of the FH signals emitted by attackers. Because the SFs cannot be reproduced, it is possible to configure non-replicable authentication systems in the physical layer of the FHSS network. This study focused on evaluating the RFEI method, one of the components of the overall authentication system. Our future study will consider system improvement by utilizing the GCAM to detect misclassification cases. As another future study, we will consider the property of the outliers in the RFEI system. We believe that further distinctions of the outliers, namely the detection of multi-labeled outliers, may be possible. We expect that this future consideration will help prevent the malicious application of the RFEI system, such as when eavesdroppers utilize the RFEI system. If the eavesdropper can successfully prepare the target FH sample, it can be used as a signal tracking method to decode the actual FH signal transmission. Our future study will consider the ways to prevent this malicious scenario by generating artificial outliers that can imitate authentication users. Conceptualization, J.K. and H.L. ([PERSON]); methodology, J.K.; software, J.K.; validation, J.K. and Y.S. ; formal analysis, J.K. and H.L. ([PERSON]); data collection, J.K., H.L. ([PERSON]) and J.P.; writing--original draft preparation, J.K., Y.S. and H.L. ([PERSON]); writing--review and editing, J.K., Y.S. and H.L. ([PERSON]); visualization, J.K.; supervision, H.L. ([PERSON]); project administration, H.L.