## 6 Conclusions  
In this paper, we proposed a DLSMR and TD-PSO protocol in FANETs with CF-mMIMO to establish a secure multicast route that improves node connectivity against wormhole attacks. The proposed DLSMR protocol utilized a DL approach to predict the secure and unsecured route based on various parameters such as node ID, distance, destination sequence, hop count, and energy which can avoid wormhole attacks. To enhance node connectivity, we proposed a TD-PSO clustering protocol that employed particle swarm optimization to find the global optimal points to maximize the cost function. This function considered the weight of the remaining energy, cosine similarity, cosine distance, and node degree, which led to electing the cluster head candidate. Furthermore, we also compared the protocol performance under two different mobility models (RPGM and RWP). The performance evaluation showed that the proposed DLSMR protocol with TD-PSO clustering protocol with the RPGM model can establish highly stable multicast trees that are robust to wormhole attacks. The proposed DLSMR protocol has better security performance than the MAODV protocol as a benchmark against wormhole attacks. This is indicated by the PDR and number of packet loss performance values which are better than MAODV. Simultaneously, the TD-PSO protocol can improve node connectivity and manage multicast members efficiently with good control overhead, number of cluster head changes, and routing delay. Overall, the proposed DLSMR protocol with TD-PSO clustering protocol under the RPGM model guarantees high stability, low routing delay, low packet loss ratio, low number of packet losses, low control overhead, and high PDR. To expand this work, we are trying to develop a secure routing protocol with a cross-layer design to protect the information against eavesdropper attacks using the DL technique.  
Figure 16: The impact of the number of nodes on the network performance metric.  
Y.P.: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing--original draft, Visualization. R.H.Y.P.: Conceptualization, Validation, Writing--review and editing. K.S.: Conceptualization, Methodology, Validation, Writing--review and editing. B.A.: Conceptualization, Methodology, Validation, Investigation, Resources, Funding acquisition, Project administration, Visualization, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2B5B01001190). [PERSON] is the corresponding author.  
Not applicable.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
AC Accept cluster  
CF-mMIMO Cell-free massive MIMO  
CH Cluster head  
CHI Cluster head information  
CM Cluster member  
DL Deep learning  
DLSMR Deep learning secure multicast routing  
DNN Deep neural network  
FANET Flying ad hoc network  
FN Flying nodes  
GN Ground nodes  
INFO Information  
JC Join cluster  
MAODV Multicast ad hoc on-demand distance vector  
MRREQ Multicast route request  
PDR Packet delivery ratio  
PSO Particle swarm optimization  
RREP Route reply  
RMSE Root mean square error  
RSSI Received signal strength indicator  
RWP Random waypoint mobility  
RPGM Reference point group mobility  
TD-PSO Top-down particle swarm optimization  
UAV Unmanned aerial vehicle  
UGW UAV gateway node