# Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security
###### Abstract
For example, the authors of [28] highlight the role of covert techniques in mitigating the detection risk posed by adversarial eavesdroppers, and the authors of [29] discuss the importance of using stochastic methods to achieve covertness in wireless communications, which demonstrates how randomisation in signal transmission can further obscure detection efforts. As wireless networks evolve, the integration of covert communications into existing security frameworks will become increasingly vital. The unique combination of PLS and covert communication techniques offers a dual-layer defence: PLS ensures that even if a message is detected, it cannot be decoded without authorisation, while covert communications reduce the likelihood that the transmission will be detected in the first place. Hence, this paper presents considerable research on the privacy of 6G at the physical layer, employing PMI and advanced machine learning (ML) algorithms, specifically deep neural networks, to predict or classify received signals based on historical data using recognition and classification of the pattern. This work extends to [PERSON] and [PERSON]'s study [16] and the development of an intelligent PLS scheme that combines random forests (RFs) for feature extraction and long short-term memory (LSTM) for sequential learning to enhance classification accuracy and handle complex data patterns while simulating three common physical layer attacks (spoofing, jamming, and eavesdropping). Additionally, we developed an anomaly detection system using isolation forests to identify unusual patterns in received signals, ensuring robust security against potential threats. This paper develops an intelligent PLS scheme utilising deep neural networks (DNNs) for detection. This lays the foundations for the 6G service-based architecture, which preserves the privacy of the shared data. This system, operating within the 6G Core Radio Access Network (RAN), enhances the security and management of received information, ensuring significant security and quality of service (QoS) via secret shared key generation and integrated key management strategies. This system works in addition to the traditional cryptographic security at the network and transport layers to facilitate lower latency of machine-to-machine (M2M) communication. Our complete contributions are as follows:  
* By utilising deep neural networks (i.e., LSTM) and a hybrid model of RF and recurrent neural networks (RNNs) (in particular, LSTM) where the hybrid model utilises RF for feature extraction and LSTM for sequential learning, we enhance classification accuracy and handle complex data patterns;* We simulated three common attacks on the physical layer: spoofing, jamming, and eavesdropping. These simulations aid in understanding the attack vectors and refining our detection mechanisms. By creating realistic scenarios, we ensure that our PLS model is well-prepared to counter these threats in real-world applications;
* We have developed an anomaly-detection system using isolation forests. This technique is capable of identifying unusual patterns in received signals that may indicate an attack or interference. By isolating anomalies, the system can quickly respond to potential threats, maintaining the integrity and security of the communication channel. This paper is structured as follows: Section 2 proposes the proposed system model. Section 3 presents and discusses the results of the system model, including the bit error rate (BER) using multi-bit codebooks for multi-antenna systems. Finally, Section 4 concludes the work and addresses the future work of this study.