Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 4 inbound Pith citation observations for arXiv:2507.04752.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.04752 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:44:21.014606Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:05:38.983314Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-10T22:30:52.969938Z

Reference resolution

15 of 15 outbound references displayed

verified exact2
verified fuzzy5
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2f26eefb-8fab-4189-a060-8e8debd3f8b6 · outbound

This paper cites NetGPT: Generative Pretrained Transformer for Network Traffic.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions NetGPT: Generative Pretrained Transformer for Network Traffic

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:20.886074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01193bb5-8bfd-471d-bc14-cc4302a65cfd · outbound

This paper cites TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:20.895166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f55399a9-db5f-47c6-b9b6-2131b848f0bf · outbound

This paper cites Pac-gpt: A novel approach to generating synthetic network traffic with gpt-3,.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions Pac-gpt: A novel approach to generating synthetic network traffic with gpt-3,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:20.905588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1eb510c3-9044-482f-8fbd-de89ea51d9dc · outbound

This paper cites Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:20.918534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14c04817-4002-4835-b3aa-5a776a1e8ae9 · outbound

This paper cites Labeling nids rules with mitre att &ck techniques using chatgpt,.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions Labeling nids rules with mitre att &ck techniques using chatgpt,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:21.429055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d1a1ac3c-2f66-439a-918c-f5c0d9e27385 · outbound

This paper cites Exploiting llm embeddings for content- based iot anomaly detection,.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions Exploiting llm embeddings for content- based iot anomaly detection,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:21.400254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fa863d05-8033-4a60-acfb-54d880f56526 · outbound

This paper cites Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:20.947913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abf4e510-030c-468f-a1ed-739d59d0a67f · outbound

This paper cites A systematic comparison of large language models performance for intrusion detection,.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions A systematic comparison of large language models performance for intrusion detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:21.375387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2631e992-56e6-4fbc-9d61-fcf60bd419b8 · outbound

This paper cites Towards Explainable Network Intrusion Detection using Large Language Models.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions Towards Explainable Network Intrusion Detection using Large Language Models

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:44:21.203148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6e0e0515-af3c-495c-85ad-195ee3cbcdd4 · outbound

This paper cites Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:20.970201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84822723-404e-48f8-9691-50d8993f652b · outbound

This paper cites Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:44:21.142227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b5bac76d-d3de-4f24-aa8d-506f6d03b01a · outbound

This paper cites Enhancing machine learning model interpretability in intrusion detection systems through shap explanations and llm-generated descriptions,.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions Enhancing machine learning model interpretability in intrusion detection systems through shap explanations and llm-generated descriptions,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:21.349149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dbe15fd1-88e3-4786-a469-0d1ce2dffb5a · outbound

This paper cites ChatIDS: Explainable Cybersecurity Using Generative AI.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions ChatIDS: Explainable Cybersecurity Using Generative AI

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:20.997864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25857972-7089-4754-8344-b4076343f7fc · outbound

This paper cites HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs).

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:21.007288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 018ee906-0d3f-4665-b072-ab5e410dc35f · outbound

This paper cites Ids-agent: An llm agent for explainable intrusion detection in iot networks,.

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions Ids-agent: An llm agent for explainable intrusion detection in iot networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:21.324296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation d873207a-3391-4fad-abc6-146fa1eb79b3 · inbound

RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking cites this paper.

RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T18:05:38.983314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc688e87-918b-463e-8e81-a104e3646f46 · inbound

MA-IDS: Multi-Agent RAG Framework for IoT Network Intrusion Detection with an Experience Library cites this paper.

MA-IDS: Multi-Agent RAG Framework for IoT Network Intrusion Detection with an Experience Library Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:30:52.974912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2fdfb8d3-b22c-4e38-ac92-302944068966 · inbound

SMT-AD: a scalable quantum-inspired anomaly detection approach cites this paper.

SMT-AD: a scalable quantum-inspired anomaly detection approach Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-13T09:28:30.950169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 193aa1b4-7359-44de-abc7-a1e7d6e5c4c6 · inbound

Attribution-Driven Explainable Intrusion Detection with Encoder-Based Large Language Models cites this paper.

Attribution-Driven Explainable Intrusion Detection with Encoder-Based Large Language Models Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:48.791017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.