Transformer-Based Intrusion Detection for Post-5G and 6G Telecommunication Networks Using Dynamic Semantic Embedding

Post-5G and 6G telecommunication infrastructures face critical information security challenges due to increasing network complexity and sophisticated cyberattacks. Traditional intrusion detection systems based on statistical traffic analysis struggle to identify advanced threats that exploit semantic-level vulnerabilities in modern communication protocols. This paper proposes a Transformer-based intrusion detection system specifically designed for post-5G and 6G networks. Our approach integrates three key innovations: First, a comprehensive feature extraction method capturing both semantic content characteristics and communication behavior patterns. Second, a dynamic semantic embedding mechanism that adaptively adjusts positional encoding based on semantic context changes. Third, a Transformer-based classifier with multi-head attention mechanisms to model long-range dependencies in attack sequences. Extensive experiments on CICIDS2017 and UNSW-NB15 datasets demonstrate superior performance compared to LSTM, GRU, and CNN baselines across multiple evaluation metrics. Robustness testing and cross-dataset validation confirm strong generalization capability, making the system suitable for deployment in heterogeneous post-5G and 6G telecommunication environments.