Large Language Model-based Network Intrusion Detection

The increasing complexity of cyber threats has made effective Network Intrusion Detection Systems (NIDS) crucial. Traditional NIDS, which rely on predefined signatures or normal network behavior, often struggle with high false-positive rates and emerging threats. This study explores integrating Large Language Models (LLMs) into NIDS to improve detection accuracy and adaptability. Fine-tuned on a comprehensive NetFlow dataset, the LLMs were evaluated using accuracy, precision, recall, and F1 score. The results demonstrate LLM- based NIDS’ potential in reducing false positives and improving novel attack detection, marking a promising direction for cybersecurity.