# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
## 5 Real-World Evaluation
### The Experiment  
We performed drone flights in two urban areas (see the flight routes for locations #1 and #2 from an altitude of 50 m in Figure 10 and 100 m in Figure 11). We performed two types of experiments; in one experiment the route was in the shape of a star (simulating a pizza delivery drone that returns to its base station after each delivery to collect a hot pizza for delivery), and in the other experiment a different, non-star-shaped route (simulating a case in which a few packages are being delivered by the drone, as might be done with an Amazon delivery drone) was used. We obtained a total of 5496 frames from an altitude  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Light** & **Linear Function** & **RMSE** & \(R^{2}\) & **MEA** \\ \hline
75\% & \(-0.163\)X + 99.575 & 3.0514 & 0.9967 & 1.4503 \\
50\% & \(-1.111\)X + 102.380 & 6.8292 & 0.9926 & 2.1720 \\
25\% & \(-1.112\)X + 101.834 & 7.1568 & 0.9923 & 1.9989 \\
10\% & \(-1.026\)X + 125.131 & 543.1094 & 0.5925 & 19.7922 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The effect of ambient light on the performance; the results are presented for an urban location, from an altitude of 50 m, under various lighting conditions (ranging from 75 to 10% light).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Light** & **Linear Function** & **RMSE** & \(R^{2}\) & **MEA** \\ \hline
75\% & \(-0.485\)X + 96.490 & 3.4121 & 0.9956 & 1.4333 \\
50\% & \(-0.486\)X + 95.730 & 4.5149 & 0.9943 & 1.6018 \\
25\% & \(-0.500\)X + 99.148 & 4.7360 & 0.9943 & 1.8010 \\
10\% & \(-0.475\)X + 123.390 & 246.8611 & 0.7533 & 13.2136 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The effect of ambient light on the performance; the results are presented for an urban location, from an altitude of 100 m, under various lighting conditions (ranging from 75 to 10% light).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Light** & **Linear Function** & **RMSE** & \(R^{2}\) & **MEA** \\ \hline
75\% & \(-0.226\)X + 98.867 & 1.3956 & 0.9981 & 0.9657 \\
50\% & \(-0.231\)X + 99.090 & 2.4037 & 0.9970 & 1.2434 \\
25\% & \(-0.235\)X + 100.492 & 2.7024 & 0.9967 & 1.2696 \\
10\% & \(-0.225\)X + 103.089 & 41.5135 & 0.9487 & 5.8316 \\ \hline \hline \end{tabular}
\end{table}
Table 12: The effect of ambient light on the performance; the results are presented for an urban location, from an altitude of 200 m, under various lighting conditions (ranging from 75 to 10% light).  
of 50 m and 5425 frames from an altitude of 100 m, each of which was associated with a specific GPS location at a point in time. For simplicity, the drone flew at a speed of approximately 4 km/h, and one frame and its GPS location were obtained per second.  
A test window is defined as the number of points needed to construct a linear regression function so that \(f(correlation)=distance\_prediction\). The subsequent frames can be predicted by correlating frames with the distance for the same window. In each experiment, we used different window sizes to create the linear regression and calculated the predicted difference in the GPS location distance. We focused on three future points and estimated their position based on the linear regression function, as doing so would allow us to raise an alert within a reasonable amount of time in the case of a GPS spoofing attack. Every calculation window utilizes resources, so we aimed to define a calculation window that optimizes the number of calculations and the false positive rate (FPR), which we would like to keep low.