Intrusion Detection Technology Based on Large Language Models

This paper explores the application of large language models, particularly BERT, to intrusion detection technology. The urgency of developing sophisticated intrusion detection systems (IDS) has grown with the rise of complex cyber threats. The paper proposes a novel framework that employs BERT to extract meaningful features from network data and identify anomalous behavior. By transforming network data into natural language text format, the model effectively discerns patterns often overlooked by traditional IDS. The research findings reveal that the BERT-based IDS significantly outperforms conventional machine learning methods in terms of accuracy, detection rate, and false positive rate. Experimental results demonstrate the model's robustness in various network environments, showcasing its versatility. Specifically, the model excels in identifying unknown threats, reducing false positives, and enhancing attack detection accuracy. These findings emphasize the potential value of large language models in the field of network security.