# Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions

arXiv: https://arxiv.org/abs/2507.04752 · HTML: https://mlsanity.com/paper/2507.04752/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [NetGPT: Generative Pretrained Transformer for Network Traffic](https://mlsanity.com/paper/2304.09513/) (2023)
- [HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs)](https://mlsanity.com/paper/2309.16021/) (2023)
- [Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging](https://mlsanity.com/paper/2402.18205/) (2024)
- [Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection](https://mlsanity.com/paper/2405.11002/) (2024)
- [TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation](https://mlsanity.com/paper/2403.05822/) (2024)
- [Towards Explainable Network Intrusion Detection using Large Language Models](https://mlsanity.com/paper/2408.04342/) (2024)
- [ChatIDS: Advancing Explainable Cybersecurity Using Generative AI](https://mlsanity.com/paper/2306.14504/) (2023)
- [Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments](https://mlsanity.com/paper/2409.11276/) (2024)
- [Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection](https://mlsanity.com/paper/2310.19658/) (2023)

## Built on by (most cited first, 4 of 4)

- [MA-IDS: Multi-Agent RAG Framework for IoT Network Intrusion Detection with an Experience Library](https://mlsanity.com/paper/2604.05458/) (2026)
- [From Flows to Words: Can Zero-/Few-Shot LLMs Detect Network Intrusions? A Grammar-Constrained, Calibrated Evaluation on UNSW-NB15](https://mlsanity.com/paper/2510.17883/) (2025)
- [Benchmarking Small Language Models and Small Reasoning Language Models on System Log Severity Classification](https://mlsanity.com/paper/2601.07790/) (2026)
- [Attribution-Driven Explainable Intrusion Detection with Encoder-Based Large Language Models](https://mlsanity.com/paper/2604.06266/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have revolutionized various fields with their exceptional capabilities in understanding, processing, and generating human-like text. This paper investigates the potential of LLMs in advancing Network Intrusion Detection Systems (NIDS), analyzing current challenges, methodologies, and future opportunities. It begins by establishing a foundational understanding of NIDS and LLMs, exploring the enabling technologies that bridge the gap between intelligent and cognitive systems in AI-driven NIDS. While Intelligent NIDS leverage machine learning and deep learning to detect threats based on learned patterns, they often lack contextual awareness and explainability. In contrast, Cognitive NIDS integrate LLMs to process both structured and unstructured security data, enabling deeper contextual reasoning, explainable decision-making, and automated response for intrusion behaviors. Practical implementations are then detailed, highlighting LLMs as processors, detectors, and explainers within a comprehensive AI-driven NIDS pipeline. Furthermore, the concept of an LLM-centered Controller is proposed, emphasizing its potential to coordinate intrusion detection workflows, optimizing tool collaboration and system performance. Finally, this paper identifies critical challenges and opportunities, aiming to foster innovation in developing reliable, adaptive, and explainable NIDS. By presenting the transformative potential of LLMs, this paper seeks to inspire advancement in next-generation network security systems.