# Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security
## 4 Conclusions and Future Work  
This study presents a cutting-edge framework for improving the security of 6G communication networks by combining deep learning techniques with PLS. We demonstrate significant improvements in classification precision and reliability over intricate data patterns by integrating multi-stage detection techniques and using deep neural networks together with a hybrid model for sequential learning. Through the simulation of spoofing, jamming, and eavesdropping attacks, we optimised the detection algorithms and developed an anomaly-detection system to identify potential threats. Our results demonstrate the advantages of ML and hybrid models over traditional approaches, with significant improvements in BER and accuracy, particularly under adversarial conditions. Future work  
Figure 3: Training and validation convergence for both the ML and hybrid models for [PERSON] and [PERSON]. The loss corresponds to the model’s error during training, representing the difference between the predicted and actual outcomes. will focus on expanding the applicability of our framework to diverse 6G communication scenarios and exploring the integration of emerging technologies such as blockchain, quantum communications, and AI/ML for enhanced security. Furthermore, additional research is required to investigate the scalability and real-world implementation of the proposed security framework. Future work can extend and further strengthen the results by addressing a broader range of attacks beyond spoofing, jamming, and eavesdropping, particularly in the context of blockchain-assisted authentication for IoT [40]. Attacks such as replay, denial of service (DoS), and man-in-the-middle (MitM) are particularly relevant to IoT environments, where security vulnerabilities at the physical and network layers can expose critical infrastructure to threats. For instance, while the system showed strong resilience against simulated attacks, its ability to handle replay attacks--where attackers can retransmit intercepted communication--was not explored. Similarly, DoS attacks, which can overwhelm blockchain-assisted authentication systems by exhausting computational resources, could be particularly disruptive in real-time 6G applications that require ultra-low latency and reliable connectivity. Lastly, MitM attacks, which intercept and alter communication between nodes, pose a significant risk in environments relying on continuous secure exchanges, such as IoT healthcare systems or autonomous vehicles. Moreover, another future piece of work aims to conduct a comprehensive comparison of our security framework with other existing methods for 6G, such as quantum communication, blockchain-based approaches, and zero-trust architectures. This will allow us to assess the relative strengths and weaknesses of our approach in terms of computational efficiency, real-time performance, and resilience against a broader range of threats. By benchmarking our framework against these alternative solutions, we aim to provide a clearer understanding of its practical applicability and effectiveness in meeting the stringent security requirements of 6G networks. Furthermore, we plan to expand our research by incorporating covert communications as a complementary security mechanism to PLS. Covert communications offer an additional layer of transmission security by making the presence of communication undetectable, which is highly relevant in the evolving landscape of 6G. This includes the exploration of relevant frameworks such as harvest and opportunistically relay- and covertness-aware trajectory design for UAVs to integrate covert communication principles into our study. This will provide a more comprehensive approach to 6G security, enhancing both the privacy and undetectability of transmissions. Conceptualization, H.M., T.I., T.B. and M.I. ; Investigation, H.M. and T.B. ; Methodology, H.M., T.I. and M.I. ; Validation, H.M., T.I., T.B. and M.I. ; Writing--Original draft, H.M., T.I., T.B. and M.I. ; Writing--Review and editing, H.M. and M.I.