# Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security
###### Abstract  
This paper presents an advanced framework for securing 6G communication by integrating deep learning and physical layer security (PLS). The proposed model incorporates multi-stage detection mechanisms to enhance security against various attacks on the 6G air interface. Deep neural networks and a hybrid model are employed for sequential learning to improve classification accuracy and handle complex data patterns. Additionally, spoofing, jamming, and eavesdropping attacks are simulated to refine detection mechanisms. An anomaly detection system is developed to identify unusual signal patterns indicating potential attacks. The results demonstrate that machine learning (ML) and hybrid models outperform conventional approaches, showing improvements of up to 85% in bit error rate (BER) and 24% in accuracy, especially under attack conditions. This research contributes to the advancement of secure 6G communication systems, offering details on effective defence mechanisms against physical layer attacks. physical layer security; 6G privacy; multi-stage detection; anomaly detection; machine learning +
Footnote †: journal: Network  
0000-0001-999-22010-1100]T. M. M.  
areas. It is worth mentioning that integrating UAVs into wireless networks requires the development of efficient algorithms that can support autonomous flight missions. The trajectory of UAVs must be optimised to maintain continuous connectivity with ground users whilst avoiding obstacles and ensuring safety. The authors in [9] propose a deep reinforcement learning (DRL) framework that optimises the flight paths of UAVs to ensure stable connections with cellular networks while accounting for environmental constraints and dynamic network conditions. The use of quantum-inspired experience replay enhances the learning process, which enables the UAV to make more informed decisions in real time. In support of this, the authors of [10] highlight the application of UAV-assisted communications in delivering high-quality services in densely populated urban environments. Their research study demonstrated how UAVs can provide on-demand connectivity during network congestion or service outages to meet the dynamic demands of users. This flexibility is particularly important for 6G, where diverse services that consist of IoT and autonomous systems will require highly dynamic network infrastructures. Although the 3 GPP standards for 6G are under development, there are high expectations for maximum data rates reaching 1 Tbps, 100-microsecond latency, and a five-fold increase in spectral efficiency compared to 5G [11]. In order to achieve these objectives, 6G systems must efficiently utilise the available spectrum, which ranges up to 300 GHz, through the gathering of frequency division duplexing (FDM) and Time division duplexing (TDM) [12]. This broad spectrum is divided into several bands: low, mid, upper-mid, mmWave, and other sub-THz bands. The emergence of 6G technologies will lead to the dense deployment of massive network infrastructures, exponentially increasing the size of the network and improving the possibilities for the Internet of Vehicles (IoV) [13, 14]. A crucial focus in the evolution of 6G-IoT is security because of the increase in malicious attacks which can severely disrupt operations [15, 16]. Potential security solutions have been proposed using AI/ML, quantum communications, blockchain and smart contracts, and zero-trust architectures [17, 18, 19]. Applying security in Layer 1 of the OSI stack, known as PLS, is another security direction and the focus of this paper. Unlike transport and network layer security protocols, such as TLS and IPSEC, which operate at Layers 3 and 4, PLS provides a lower latency method for exchanging secret information. This method utilises the distinctive features of the transmitter-receiver channel as a secret key for higher-level encryption algorithms [16, 20].