Anomaly-Based Intrusion Detection System Using RAG

This article presents a new method for preventing network breaches using Retrieval-Augmented Generation (RAG) technology. The system combines machine learning and large language models to provide better protection through intelligent monitoring and clear explanations.

Details

The article discusses the growing need for robust network security systems to address the increasing frequency of cyber threats and attacks. Traditional intrusion detection systems (IDS) rely on predefined rules and static machine learning models, which lack flexibility and provide unclear decision-making processes. The proposed solution uses RAG technology to create an anomaly-based IDS that can detect both known and new attack patterns. The system first preprocesses the NSL-KDD dataset, a widely used benchmark for network security research, and creates a knowledge base of historical attack data. When a new network input is received, the system retrieves the five most similar past records and uses a large language model to classify the input as normal or an attack, while also providing a comprehensive explanation. The key advantages of this system include the ability to identify emerging threats, deliver human-understandable explanations, and improve decision-making capabilities for security analysts. The system's performance is evaluated against traditional models, demonstrating superior accuracy, precision, recall, and F1-score metrics.