## 1 Introduction
### Related Works  
Some works have been studied to improve network stability in FANETs. The authors of [20] proposed bio-inspired clustering for the FANET environment. They develop a bio-inspired clustering protocol to improve energy efficiency (EE) and manage UAV mobility. However, it does not consider safety considerations to reduce collisions and enhance stability. In [21], the authors proposed an intelligent cluster routing scheme (CRSF) to address UAV communication issues. Additionally, the CRSF can improve the stability performance in UAV communication. Nevertheless, the CRSF has a high control overhead during the clustering process. The authors of [22] used clustering as a solution to improve network stability. In addition, they also studied EE localization and clustering for UAV wildfire monitoring. The authors of [23] proposed a down-up particle swarm optimization to improve the scalability of the FANET system. However, this work only considered the optimization approach to improve stability performance. Regarding secure multicast routing protocols, improved security performance in the FANET system was studied in [24]. Scalable and predictive routing (SP-GMRF) was proposed to predict the next node. Based on node position, the SP-GMRF provided the shortest distance to each destination node. However, SP-GMRF does not consider the clustering protocol, so it has a high control overhead. The authors of [25] proposed a distributed tree-based routing (DPTR) for FANETs, forming a network that avoids fragmentation. In this approach, a tree-like structure connects all the UAVs in the networks. Nevertheless, the routing delay is high since every node calculates the entire routing tree. In [26], the authors studied swarm EE multicast routing (SEMRP) for UAV swarms for UAVs in group formation. The SEMRP can reduce packet loss and packet re-transmission, and delay performance. However, the SEMRP has a high control overhead during the routing process. The authors of [27] proposed a credible neighbor discovery (CRFNE) algorithm to shield messages against wormhole attacks in wireless sensor networks (WSNs). The CRFNE can detect wormhole attacks during route discovery. However, the CRFNE has a high processing time due to the greater number of steps to calculate the wormhole threshold to detect the wormhole node. Based on the AODV routing protocol, the authors of [28] designed a wormhole-immune routing protocol. However, the DAWA protocol has a high control overhead because the DAWA protocol broadcasts the packet discovery to all nodes in the network. Meanwhile, the authors of [29] proposed a hybrid price auction (HPA)-based routing protocol to avoid sinkhole attacks. In addition to that, the HPA protocol can improve the security, routing delay, and scalability performance. Nevertheless, they only consider sinkhole attacks in the network. On the other hand, the DL is applied to improve end-to-end (E2E) delay in aeronautical ad hoc networks (AANETs) [30]. The authors of [30] explored a DL-based multicast routing protocol for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. However, they have not considered wormhole attacks in the networks. Additionally, the authors of [31] studied reinforcement learning-assisted secure routing to minimize delays and map geographic information using Q learning. Thus, the proposed protocol can select the secure route efficiently. However, they did not mention which kind of attack was considered. The above-mentioned works partially addressed the raised network issues such as secure multicast routing, network scalability, and deep learning approaches.