# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 3 Security and Safety Issues
### Public Safety Applications
Moreover, secure wireless communication is established using a secure component at the UAV, enabling the cryptographic protocols of the compressed video stream. Then, initial real-time evaluation of the collected data is performed at the cloud and CNN-based detection is developed for achieving an improved trade-off amongst detection accuracy and latency. The performance of proposed solutions is evaluated on a hardware platform based on an Infineon Larix drone and a Raspberry Pi connected to a physical secure element (SE) responsible for the cryptographic protocols, a camera, and a graphics processing unit (GPU) server. Performance evaluation showed that secure communication, fast detection, and a user-friendly UI can facilitate the emergency response in civilian applications. Moreover, a trade-off between an accuracy above 65% of the mean average precision and a processing time below 150 ms can be achieved when data processing relies on the Kullback-Leibler (KL) divergence metric instead of root mean square distance (RMSD). Another critical application was examined in Reference [59], aiming to overcome privacy and public safety concerns arising from the use of UAVs by amateurs over sensitive areas. Towards this end, a distributed system was proposed for identifying and locating dangerous UAVs based on wireless acoustic sensors and SVM-based ML algorithms. Then, the plethora of RF characteristics and telemetry data of amateur UAVs was analyzed using a software-defined radio (SDR) platform adopting a SVM-based ML approach, deriving classifiers to specify if a transmitted packet came from an UAV. After the telemetry protocol was decoded, the system transmitted control commands to the UAVs and, if they insisted on their malicious behavior, aggressive surveillance UAVs were deployed. From the simulations, it was suggested that the proposed ML-enhanced SDR platform enables efficient and proactive protection of the sensitive area against trespassing UAVs. More specifically, the acoustic detection performance of UAVs adopting the SVM-based solution is satisfactory but demands relatively high signal-to-interference ratio (SIR). Also, the acoustic location performance is severely affected by Gaussian noise and erroneous signal latency measurements. Finally, the identification of a telemetry data packet presents significant fluctuations, depending on the bit error rate. Finally, the study in Reference [60] proposed the deployment of low-cost surveillance UAVs equipped with smartphones for small boat detection, which is of high importance in search and rescue as well as border patrol operations. More specifically, the UAVs transmitted images using the cellular infrastructure to a distant cloud server. In the cloud, a CNN was trained and detected various classes of small boats as well as people in order to trigger security alerts. Moreover, the proposed system included an automatic identification system (AIS) for comparing images of the detected boats and for comparing its GPS position with a marine traffic database, classifying it either as a registered or unregistered boat. For enhanced security of the signal transmission, various encryption mechanisms were integrated in the system, guaranteeing data integrity while jamming attacks were mitigated through Gaussian noise overlapping. More importantly, the proposed light NN offers up to 99% accuracy of object detection, greatly facilitating the tasks of search and rescue as well as border patrol operations. Table 3 summarizes UAV security issues that were investigated in the literature and the corresponding ML solutions that were adopted to address them.