This paper introduces a secure communication architecture for Unmanned Aerial Vehicles (UAVs) and ground stations in 5G networks, addressing critical challenges in network security. The proposed solution integrates the Advanced Encryption Standard (AES) with Elliptic Curve Cryptography (ECC) and CRYSTALS-Kyber for key encapsulation, offering a hybrid cryptographic approach. By incorporating CRYSTALS-Kyber, the framework mitigates vulnerabilities in ECC against quantum attacks, positioning it as a quantum-resistant alternative. The architecture is based on a server-client model, with UAVs functioning as clients and the ground station acting as the server. The system was rigorously evaluated in both VPN and 5G environments. Experimental results confirm that CRYSTALS-Kyber delivers strong protection against quantum threats with minimal performance overhead, making it highly suitable for UAVs with resource constraints. Moreover, the proposed architecture integrates an Artificial Intelligence (AI)-based Intrusion Detection System (IDS) to further enhance security. In performance evaluations, the IDS demonstrated strong results across multiple models with XGBoost, particularly in more demanding scenarios, outperforming other models with an accuracy of 97.33% and an AUC of 0.94. These findings underscore the potential of combining quantum-resistant encryption mechanisms with AI-driven IDS to create a robust, scalable, and secure communication framework for UAV networks, particularly within the high-performance requirements of 5G environments.

Abstract--In a previous paper, we have shown that a recurrent neural network (RNN) can be used to detect cellular network radio signal degradations accurately. We unexpectedly fou nd, though, that accuracy gains diminished as we added layers to the RNN. T o investigate this, in this paper, we build a parall el model to illuminate and understand the internal operation o f neural networks, such as the RNN, which store their internal state in order to process sequential inputs. This model is wi dely applicable in that it can be used with any input domain where the inputs can be represented by a Gaussian mixture. By looki ng at the RNN processing from a probability density function perspective, we are able to show how each layer of the RNN transforms the input distributions to increase detection a ccuracy. At the same time we also discover a side effect acting to limit the improvement in accuracy. T o demonstrate the fidelity of t he model we validate it against each stage of RNN processing as well as the output predictions. As a result, we have been able to explain the reasons for the RNN performance limits with usef ul insights for future designs for RNNs and similar types of neu ral network. In the latest generation of cellular networks, 5G, the emergence of sophisticated new techniques such as large scale MIMO and multicarrier operation has resulted in rapid growth in the total number of radio access network (RAN) configuration parameters. This carries with it a considerab le risk in terms of potential misconfiguration and is likely to significantly add to the workload for network management teams. Fortunately the recent emergence of powerful machin e learning techniques has the potential to counter this by ale rting operators to issues which might not otherwise be apparent an d providing assistance to resolve them in a timely manner. In our earlier work [1], we showed that it is possible to apply a recurrent neural network (RNN) to address an issue of particular concern to mobile network operators, namely how to detect cell performance degradations which are not being reported to the network control centre but are impairi ng the quality of service perceived by the users.

In this paper, the problem of drone-assisted collaborative learning is considered. In this scenario, swarm of intelligent wireless devices train a shared neural network (NN) model with the help of a drone. Using its sensors, each device records samples from its environment to gather a local dataset for training. The training data is severely heterogeneous as various devices have different amount of data and sensor noise level. The intelligent devices iteratively train the NN on their local datasets and exchange the model parameters with the drone for aggregation. For this system, the convergence rate of collaborative learning is derived while considering data heterogeneity, sensor noise levels, and communication errors, then, the drone trajectory that maximizes the final accuracy of the trained NN is obtained. The proposed trajectory optimization approach is aware of both the devices data characteristics (i.e., local dataset size and noise level) and their wireless channel conditions, and significantly improves the convergence rate and final accuracy in comparison with baselines that only consider data characteristics or channel conditions. Compared to state-of-the-art baselines, the proposed approach achieves an average 3.85% and 3.54% improvement in the final accuracy of the trained NN on benchmark datasets for image recognition and semantic segmentation tasks, respectively. Moreover, the proposed framework achieves a significant speedup in training, leading to an average 24% and 87% saving in the drone hovering time, communication overhead, and battery usage, respectively for these tasks.