Abstract
Unprecedented security challenges were offered by the rapid evolution of 6G networks. These unprecedented security challenges, especially segmented attacks (SA), exploit network susceptibilities. Here, advanced detection and migration methods are needed to ensure robust security. Here, the limited potential of the limited feature extraction (FE) and feature classification of the intrusion detection (ID) systems (IDS) may result in the lack of real‐time (RT) adaptability. This IDS also fails to detect advanced segmentation‐based threats accurately. For 6G networks, an AI‐driven intrusion detection system (IDS) with deep packet inspection (AI‐IDS‐DSP) is suggested in this paper. This suggested method will assist in overcoming those limitations. Then, the digital signal processing (DSP) techniques are also integrated into this suggested method, and this integration will help in analyzing signal anomalies. Those DSP methods include wavelet transforms (WT) and Fourier transforms (FT). Then, the hybrid AI model (CNN + Transformer) is utilized by the suggested method for the purpose of anomaly detection (AD). The application of reinforcement learning (RL) may enhance the adaptive security measures in the RT. Finally, the sensitive financial transactions are secured by the suggested robust network security (NS) method. This suggested NS application will help in preventing single account (SA) issues and offers proactive detection. The data integrity (DI) in university financial management systems were also implemented by this suggested NS method.