# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 3 Security and Safety Issues
### Public Safety Applications  
Due to the flexible deployment of UAV networks, several public safety applications rely on flying nodes, acting as BSs for establishing communication after a disaster or as monitoring stations for assessing the condition of critical infrastructures and environmental attributes. In Reference [56], a defense system for tackling malicious UAVs was proposed. The defense system employed various UAV detection techniques, such as jamming, GPS spoofing, transmission of hacking signals, and laser shooting. Then, its interaction with malicious UAVs was cast as a dynamic defense game, where the appropriate defense policy was chosen, considering the attack mode of the UAV and the significance of the protected area. Towards this end, the author adopted the Markov decision process (MDP) framework to model the defense policy selection. Also, Q-learning is used to determine the optimal defense policy without requiring any prior knowledge of the current attack mode of the UAV in the dynamic game. The simulations that were conducted indicated that the proposed RL-based malicious UAV interception scheme reduced the risk rate of the protected area and improved the defense system's utility when compared to a random defense policy. Then, in disaster mitigation and management scenarios, where rapid changes in the environment usually occur, UAV swarms can be employed for effective and real-time mapping of complex and large-scale zones. In this regard, ML-based modeling and simulation techniques for an UAV swarm system with autonomous capabilities were presented in Reference [35] and the decision-making procedures were distributed among all the swarm members. In particular, an efficient mapping procedure based on genetic algorithms searching on a population of potential solutions was proposed to optimize the information gathering and to minimize the percentage of unmapped sites in the area of interest. Next, the work in Reference [57] focused on the issue of UAV hijacking and compared various ML approaches to analyze and classify the behavior of different UAV pilots. To generate data, twenty UAV pilots were employed to fly the same UAVs in three specific trajectories. Pilot identification relied on the way that each pilot controlled the UAV and not on their specific actions, i.e., the structure, cadence, and timing of command patterns. By assuming that the pilots had to carry out identical maneuvers and lossless recording of their behavior, fast identification was possible using five ML classifiers, i.e., linear discriminant (LD), quadratic discriminant (QD), SVM, KNN, or RandF. From the comparisons, it was concluded that the RandF classifier provided the best accuracy at around 90%, exploiting simple time-domain features. More importantly, the classification exhibited low complexity, and thus, it was suggested that it can be performed on-board the UAV for achieving real-time interception and prevention of hijacking. However, the accuracy is strongly related with the pilot and the flight trajectory, as well as the pitch, roll, and yaw effects. Moreover, thrust control signals exhibit varying levels of significance for pilot identification. In addition, the authors of Reference [58] aimed at integrating UAV surveillance capabilities with a cloud-computing environment and an appropriate user interface (UI) for efficient emergency response. More specifically, the authors assumed limited computing and energy capabilities at the UAV and, so, computation offloading of the acquired data was performed at the cloud. Security issues in this scenario are related to the interception by malicious nodes of the UAV data, threatening their privacy, confidentiality, and integrity. In order to overcome such concerns, the end-to-end solution is based on on-site video data collection by the UAV, which employs a keyframe selection algorithm for video compression and latency reduction.