# Security Establishment in ADS-B by Format-Preserving Encryption and Blockchain Schemes
## 4 Experimental Results  
Detailed experiments were performed to evaluate system performance and to measure security levels. These experiments assessed the following four aspects:  
* Entropy and security complexity: In cryptography, entropy is frequently used to measure a bit string's level of uncertainty. In addition, serial correlation and plain and encrypted data correlation are key indicators for security measurement. * Sensitivity: The minimum trigger level (MTL) of an ADS-B receiver processing signals within the frequency span of 1089 to 1091 MHz must respect the MTL constraint, which is \(-\)84 dBm for ADS-B class \"A3\" [26]. * Computational time: Encryption increases the time required for message transmission and reception at both ends. Therefore, it is essential to verify the impact of such increased processing time on both ADS-B Out/In. * Waveform comparison: Undoubtedly, applying the FFX algorithm alters the bit sequence in time, so it is wise to observe the spectrum accordingly. Entropy measurements were performed on both plain data and encrypted data of real flight tests according to ADS-B Out logs. This flight test was performed in a light aircraft (Cessa C-172) flying from Saint-Hubert (CYHU) Airport to Mirabel (CYMX) Airport, as shown in Figure 7 [30]. It is worth noting that the entropy is only computed for the \"odd\" and \"even\" CPR position messages. In addition, the entropy of messages is measured in bits per byte. The entropy or information content is computed according to Formula (1). \[H(X)=-\sum_{x_{i}\in X}p(x_{i})log_{2}p(x_{i}) \tag{1}\]  
Figure 6: Blockchain network topology for key and data exchange. Figure 8 illustrates plain and cipher data entropy for various scenarios. For the 14-byte experiment, the cipher data produced an entropy of 7.9985 bits per byte, whereas the plain text messages had an entropy of 6.7036 bits per byte. These results are similar to the experimental results found by [PERSON] et al. [12]. Moreover, as the cipher data length decreases, the entropy value decreases accordingly. A low entropy value signifies that encrypted data is more vulnerable to possible attacks. Apart from entropy, the serial correlation of plain and cipher data and the correlation between plain and cipher data are extracted from ADS-B Out flight information, both of which are presented in Figure 9. As revealed in Figure 9, there is a strong serial correlation among the plain data, while the cipher data serial correlation value has an inverse relation with the length of the cipher data. More specifically, the cipher data's serial correlation value with longer data length is less than its correlation with shorter data size. As expected, plain and cipher data correlation follows the same trend as cipher data serial correlation. Therefore, a higher correlation value, particularly in the case of ICAO encryption, is more vulnerable to codebook attacks. The second experiment was performed to evaluate both standard ADS-B In and SADS-B In signal sensitivity. Figure 10a is a diagram of the test bench designed to evaluate signal sensitivity [30]. As the block diagram illustrates, the ADS-B Out generates ADS-B signals, which are then weakened by a variable attenuator. Next, a splitter provides two signal outputs with the same signal strength, Out1 and Out2. Out1 is used as the input of ADS-B In and Out2 is used as the spectrum analyzer input for signal power measurements. The same test is done for SADS-B. The graph in Figure 10b shows that there are no changes in  
Figure 8: Plain and cipher data entropy. Figure 7: Flight test route. either standard ADS-B's or SADS-B's sensitivity criteria. The Accuracy is calculated based on the following formula:  
\[Accuracy=100*\frac{(count\ of\ correctly\ received\ ADSB\ packets)}{(total\ count\ of\ tranmissted\ ADSB\ packets)} \tag{2}\]  
A third experiment was conducted to measure the computation time for both standard ADS-B and SADS-B. Table 2 lists the mean values of the computational time for both systems in MATLAB Simulink and C++.