Enhanced security framework for 6g communication network based on dual key extraction-based NOMA technique with two- tier encryption and optimized ant fly optimizer (AFO) keys

This research introduces a pioneering framework for securing communication networks for 6G communication network, through the utilization of a Dual Key Extraction (DKE)-based Non-Orthogonal Multiple Access (NOMA) technique. With the advent of the 6G networks that are anticipated to provide ultra-fast data transfer, connect numerous devices, and have minimum latency, there are formidable security challenges such as increased espionage activities and hacking. The core of this approach involves the generation of two keys, where the first key is intricately linked to channel randomness, and the second key is derived based on distance considerations. Among the generated keys, the optimal keys utilized for data encryption is selected dynamically based on the new Ant Fly Optimizer (AFO) approach. In addition, the encryption is performed using the new two- tier security model that encapsulates the Two-fish encryption (TFE) and the new Elliptic Curve Diffie-Hellman (ECDH). In addition, the error correction within the encrypted data is accomplished via Galois approach. In addition during the transmission stage, the integration of non-binary Raptor codes for handling packet loss and errors in the data channels, while the Galois Field-Based Error Correction is applied for ensuring mathematical consistency and error resilience in cryptographic encoding.. These elements collectively contribute to fortifying the security of communication channels against potential eavesdropping threats. The proposed system establishes a secure communication channel between the Base Station (BS) and end-users, minimizing the risk of unauthorized access. To address potential errors, such as bit flipping, a critical bit reconciliation technique, namely Raptor codes, is integrated dense 6G environments. The implementation of secured 6G model is performed using the MATLAB platform. The proposed model achieved the key mismatch rate of 0.0001% for SNR value of 30dB, which is lower than the existing techniques.