# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 5 Experimental Results on the TEXBAT Dataset
### Receiver Operating Characteristic Curve Experiment
Among the methods, the EDDM-MSC-MMP has the fastest response time, followed by CN0, Ratio+ELP, and Ratio. The response times of the ELP, Delta, Slope, and Delta+Slope methods are quite similar. With respect to the detection rate, following the detection of the spoofing signal, the detection rate curves of the methods exhibit a peak shape, except for that of the EDDM-MSC-MMP; this method maintains a high detection rate of 100%. The curves of the other methods are characterized by an initial increase in the detection rate, followed by a decrease. The maximum detection rate achieved by these methods does not exceed 50%. The reason for the difference between the curves in Case 2 and Case 1 is that, in Case 2, the spoofing signal coexists with the real signal, and the greater power of the spoofing signal enables it to suppress the real signal. Because their carriers are out of sync, the receiver is unable to immediately and completely lock onto the spoofing signal, which causes a small distortion in the correlation peak function over a short period. Therefore, although the method based on the correlation peak function shows some detection rates, its overall detection performance is not satisfactory. The distinction between Case 3 and Case 2 lies in the utilization of a matched-power scheme, in which the power of the spoofing signal decreases from 10 dB to 1.3 dB, whereas the phase difference between the carrier of the spoofing signal and the real signal remains unaltered. In Case 3, a notable increase in detection efficacy is observed when the correlation peak function and the CN0 method are employed (Figure 8). This improvement can be attributed to the reduction in the power advantage of the spoofing signal in Case 3 to 1.3 dB, coupled with a fixed phase difference with the real signal. This increases the time  
Figure 7: Detection rate of each detection method for Case 2 of the TEXBAT dataset. required for the receiver's phase-locked loop to track the spoofing signal, resulting in an increase in the distortion time of the correlation peak function. As a result, the correlation peak detection method can more easily identify spoofing signals. In Case 3, the curves of all detection methods showed significant fluctuations. After the spoofing signal intrusion, the detection rates of CN0, Slope, Delta, ELP, and Slope+Delta rapidly decreased to nearly zero. In contrast, the EDDM-MSC-MMP, Ratio, and Ratio+ELP were able to maintain a certain detection rate. To conduct a comprehensive evaluation of the performance of the various detection methods, we calculated and simulated the ROC curves for Cases 1, 2, and 3. During the interval of 0 to 410 s designated for spoofing detection, the corresponding detection rate \(P_{d}\) and false alarm rate \(P_{f}\) were calculated by adjusting the threshold of the detection method, and the ROC curve was plotted. The ROC curves for Cases 1, 2, and 3 are presented in Figures 9-11, respectively. To evaluate the detection performance, we conducted a further analysis of the area under curve (AUC) for the receiver operating characteristic (ROC) curves, which has a theoretical maximum value of 1. The AUC value of the EDDM-MSC-MMP method is markedly higher than those of the other methods, exceeding 0.94 in all cases. Notably, it reaches 0.994 in Case 2, approaching the ideal value of 1. In comparison, the AUC values of the alternative detection methods range from 0.65 to 0.9. The combination of these metrics yields the following performance ranking from high to low: EDDM-MSC-MMP > Ratio+ELP > Ratio > CN0 > Slope+Delta > ELP > Slope > Delta. In conclusion, the EDDM-MSC-MMP achieves a better ROC curve performance than the other detection methods in all the cases, including cases of suppressed spoofing signals, highly similar spoofing signals, and spoofing signal switching.