An Intrusion Detection Method Based on Transformer and Transfer Learning

To overcome the limitations of existing intrusion detection systems, particularly in the areas of encrypted traffic analysis, cross-domain adaptation, and small-sample learning scenarios, this study proposes the TTL-IDS model, which integrates the Transformer architecture with transfer learning techniques. The model incorporates a multi-head self-attention mechanism with position encoding to effectively capture long-range dependencies in network traffic, a critical capability for identifying subtle and complex attack patterns. Furthermore, a hierarchical feature transfer framework is introduced, leveraging domain adversarial training to facilitate robust knowledge transfer from the source domain to the target domain. Experimental results validate the effectiveness of TTL-IDS in enhancing detection accuracy and domain generalization. This research not only demonstrates the model’s practical advantages but also offers novel insights and methodologies for strengthening security in dynamic and heterogeneous network environments.