# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 3 Security and Safety Issues
### Physical Layer Security (PLS)  
PLS has been proposed as an efficient way of safeguarding the operation of wireless networks against a variety of attacks, ranging from jamming to passive and active eavesdropping [48; 49]. More specifically, PLS complements encryption solutions through the exploitation of information theoretic methods in order to enhance the secrecy level of wireless transmissions [50]. Focusing on UAV networks, various studies have investigated the application of PLS solutions. In Reference [51], a UAV is employed to perform a smart attack against a source-destination pair. The UAV is capable of overhearing the transmission of the source while generating a spoofing or jamming signal to reduce the quality of the destination's reception. In order to evaluate the impact of practical assumptions, imperfect channel estimation is considered due to limited pilot signals. In this setting, a secure communication game is formulated and channel estimation errors as well as the UAV smart attacks are jointly tackled through Q-learning. By exploiting the transmission history data, transmit power adaptation is performed to enhance the secrecy of the transmission. In addition, the NE strategy of the noncooperative game under the channel estimation errors was developed, maximizing utility function of the source while mitigating the effect of the UAV smart attacks. Simulation results illustrated that the proposed Q-learning-based strategy enhances the PLS performance, decreasing the attack rate regardless of the channel estimation error. Nonetheless, in order to guarantee the convergence of the learning algorithm, knowledge of the action space of each player is required. Next, the issue of smart attacks on UAV-enhanced vehicular ad hoc networks (VANETs) has been studied in Reference [52]. There, a UAV acted as a relay for forwarding the messages of an on-board \begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Communication Target** & **ML Solution** \\ \hline [PERSON] et al. [36; 37] & UAV RSS prediction & ANN with DE LM training \\ \hline [PERSON] et al. [38] & Air-to-ground channel modeling & Unsupervised online \\ \hline [PERSON] et al. [39] & Radio map construction & Segmented regression \\ \hline [PERSON] et al. [40] & PL prediction in irregular terrains & ANN with LiDAR \\ \hline [PERSON] et al. [41] & Air-to-air PL prediction (RF) & RandF and KNN \\ \hline [PERSON] et al. [42] & Air-to-ground PL prediction (mmWave) & RandF and KNN \\ \hline [PERSON] et al. [43] & Efficient opportunistic UAV channel access & Distributed log-linear \\ \hline [PERSON] et al. [44] & Air-to-ground interference mitigation & ESN-based RL \\ \hline [PERSON] et al. [45] & Interference management among LTE-U & Regret-based \\  & and Wi-Fi networks & \\ \hline [PERSON] et al. [46] & Optimized AMC & DL with CNN and LSTM \\ \hline [PERSON] et al. [47] & Robust and energy-efficient precoding & CE-based \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Communication issues in ML-enhanced UAV networks. unit (OBU) to a roadside unit (RSU) when the latter experiences severe interference or jamming. The UAV-smart jammer interaction resulted in an anti-jamming UAV relay game, where the UAV determined its relay decision towards a different RSU and, at the same time, the jammer observed this strategy and selected an appropriate jamming power level. The NE of this game was derived showing the dependence of the optimal relay strategy on the transmission cost and the air-to-ground channel model. For the selection of the optimal relay strategy against jamming, a hotbooting policy hill climbing (PHC)-based learning approach was designed without requiring knowledge of the UAV channel model and the jamming strategy. Simulation results revealed that PHC-based relaying can reduce the bit error rate performance of the VANET compared with a scheme based on Q-learning. Further research on security aspects of coexisting VANETs and UAVs has been conducted in Reference [53].