## 3 System Model
### Eavesdropping and Jamming Model  
Based on the power constraint of UAVs, the suspicious UAVs' selection for eavesdropping and jamming is an important parameter to be considered in the following algorithm. The optimal selection depends on the \(UAV_{L}\)'s position at time slot \(x\). There are four cases as follows:  
**Case 1:**\(UAV_{L}\) eavesdrops and jams \(UAV_{ST}\). As shown in Figure 2a, \(UAV_{L}\) only chooses \(UAV_{ST}\) for eavesdropping and jamming. According to References [19; 38], at time slot \(xth\), the channel gain from \(UAV_{ST}\) to \(UAV_{SR}\), which was denoted as \(H_{s}(x)\), is expressed as:  
\[H_{s}(x)=\frac{\lambda H_{s}(x-1)+n\sqrt{1-\lambda^{2}}}{D^{\alpha_{2}}} \tag{4}\]  
where \(\alpha_{2}\) denotes the path-loss exponent in the suspicious link and \(\lambda\) presents the coefficient which adjusts two components: the weights of the auto-correlated and the independent. \(n\) is a Gaussian random number generated by Additive White Gaussian Noise (AWGN). For the suspicious communication link, we define Signal to Interference plus Noise Ratio (SINR) at \(UAV_{ST}\) at time slot \(x\) as \(\gamma_{s}(x)\), which is given by  
\[\gamma_{s}(x)=\sqrt{\frac{H_{s}(x)\cdot K_{2}^{-1}\ln\frac{K_{1}}{\epsilon} \cdot\left(2^{\rho(x)}-1\right)}{N_{0}+P_{L}(x)}} \tag{5}\]  
Figure 4: The illustration of distance when \(\theta>\pi/2\). Figure 5: The illustration of dynamic mobility of suspicious UAVs. where \(\rho(x)\) denotes the adaptive modulation and coding (AMC) rate of the \(UAV_{ST}\) at time slot \(x\), and the highest mode is denoted by \(\rho_{M}\). \(K_{1}\) and \(K_{2}\) are two constants related to the channel. \(N_{0}\) denotes the power of white Gaussian noise. \(\epsilon\) is the required instantaneous bit error rate. As elaborated in the assumption part, the suspicious UAVs consider the \(UAV_{L}\)'s eavesdropping signal as interference during the wireless communication. Hence, the eavesdropping power at time slot \(x\) is a part of interference in suspicious communication. Another part of interference is the jamming power from \(UAV_{L}\). Therefore, the interference power at time slot \(x\) is denoted as \(P_{E}(x)+P_{J}(x)\). Likewise, at time slot \(x\), the channel gain in the eavesdropping and jamming links, i.e., from \(UAV_{ST}\) to \(UAV_{L}\), is given by  
\[H_{e}(x)=H_{j}(x)=\frac{\lambda H_{e}(x-1)+n\sqrt{1-\lambda^{2}}}{d_{1}^{\alpha _{1}}(x)} \tag{6}\]  
where \(n\) is a Gaussian random number generated by AWGN. \(\alpha_{1}\) denotes the path-loss exponent. \(d_{1}(x)\) is the distance between \(UAV_{L}\) and \(UAV_{ST}\) at time slot \(x\), which can be acquired by Equation (1). As the relative position of \(UAV_{L}\) to \(UAV_{ST}/UAV_{SR}\) changes from time to time, there are two components in the eavesdropping link, which named as auto-correlated component and independent component. The former relies on the previous channel condition and the latter is independent of previous channels. The two components are adjusted by a coefficient \(\lambda\). Moreover, \(\lambda\) decreases with the growth of the speed of \(UAV_{L}\). We define Signal to Noise Ratio (SNR) of the eavesdropping and jamming links at time slot \(x\) as \(\gamma_{e}(x)\), which is  
\[\gamma_{e}(x)=\gamma_{j}(x)=\sqrt{\frac{H_{e}(x)\cdot K_{2}^{-1}\ln\frac{K_{1 }}{\epsilon}\cdot\left(2^{\rho(x)}-1\right)}{N_{0}}} \tag{7}\]  
According to the regression model proposed in Reference [20], the PRR of suspicious data packets eavesdropped by \(UAV_{L}\), which was denoted as \(R(x)\), is given by  
\[R(x)=\left(1-\frac{1}{2}exp^{-\beta_{0}\gamma_{e}(x)+\beta_{1}}\right)^{8(2f- l)} \tag{8}\]  
where \(\beta_{0}\) and \(\beta_{1}\) are two constants in the regression model. Moreover, \(\beta_{0}\) controls the shape of the regression curve and \(\beta_{1}\) induces horizontal shifts of the curve. \(f\) and \(l\) denote frame size and preamble size of the data packet, respectively. **Case 2:**\(UAV_{L}\) eavesdrops \(UAV_{ST}\) by jamming \(UAV_{SR}\).