Paper Citation Record · LEDGER

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models

As of 11 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2412.10978.

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

pith.paper-citation-record.v1
2412.10978 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:28:38.383642Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T06:41:21.097683Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

verified exact3
verified fuzzy34
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b5bff682-df3b-4d32-8bed-7bf91ef24e0b · outbound

This paper cites Study of snort-based IDS.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Study of snort-based IDS

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.381051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f4d77621-c8b0-499b-a0ba-0f2a36f42335 · outbound

This paper cites Attack hypothesis generation.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Attack hypothesis generation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.374243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d5c3c234-007a-4bc5-a7bd-e7d4cc1afbca · outbound

This paper cites Attack tactic labeling for cyber threat hunting.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Attack tactic labeling for cyber threat hunting

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.367519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d5da5167-51c3-4704-97ac-95e2380a0c07 · outbound

This paper cites Introducing uwf-zeekdata22: A comprehensive network traffic dataset based on the MITRE ATT&CK framework.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Introducing uwf-zeekdata22: A comprehensive network traffic dataset based on the MITRE ATT&CK framework

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.359938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d59a085c-cf8d-47fc-9456-8604b8bd5ef5 · outbound

This paper cites Crowdstrike introduces Charlotte AI, Generative AI Security Analyst - Crowdstrike, May 2023.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Crowdstrike introduces Charlotte AI, Generative AI Security Analyst - Crowdstrike, May 2023

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.352703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5991f959-9497-413c-870f-19a85dad82be · outbound

This paper cites ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T15:28:37.797060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 500b048f-296a-4eff-a7cf-be9d029632f0 · outbound

This paper cites Evaluating ChatGPT4 in canadian otolaryngology-head and neck surgery board examination using the CVSA model.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Evaluating ChatGPT4 in canadian otolaryngology-head and neck surgery board examination using the CVSA model

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.344077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 94940e90-eca4-4bfb-9ed1-8d64ad2628ab · outbound

This paper cites Datasets are not enough: Challenges in labeling network traffic.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Datasets are not enough: Challenges in labeling network traffic

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.238174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0740d206-389e-4cbe-af06-a1e1abc777e0 · outbound

This paper cites Generating labelled network datasets of apt with the mitre caldera framework.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Generating labelled network datasets of apt with the mitre caldera framework

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.168910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 71ea73aa-b331-4d7b-a588-e5e802222032 · outbound

This paper cites Practical threat intelligence and data-driven threat hunting.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Practical threat intelligence and data-driven threat hunting

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.161837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fcd2b9f6-8e91-4eb0-b4fd-369564f8b1b4 · outbound

This paper cites Threat intelligence: Collecting, analysing, evaluating.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Threat intelligence: Collecting, analysing, evaluating

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.153577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7493ec7a-6cc8-4970-87f8-45d3cb9a4310 · outbound

This paper cites Automated Mapping of CVE Vulnerability Records to MITRE CWE Weaknesses.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Automated Mapping of CVE Vulnerability Records to MITRE CWE Weaknesses

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:28:38.563994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dae15cb8-b240-4405-9d6c-404077d17e2f · outbound

This paper cites Ttp-based hunting.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Ttp-based hunting

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.146395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 77e4bec3-7f81-4b68-977a-eab01862f9e4 · outbound

This paper cites Attack hypotheses generation based on threat intelligence knowledge graph.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Attack hypotheses generation based on threat intelligence knowledge graph

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.139200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 311e1a82-08a1-4161-af32-08113291fc2a · outbound

This paper cites Acing the IOC game: Toward automatic discovery and analysis of open-source cyber threat intelligence.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Acing the IOC game: Toward automatic discovery and analysis of open-source cyber threat intelligence

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.131647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 12166631-d15a-409c-858e-6ffdb0354824 · outbound

This paper cites MITRE ATT&CK®: Design and philosophy.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models MITRE ATT&CK®: Design and philosophy

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.124425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8bef27a9-dce1-499c-88f1-565035a7c84c · outbound

This paper cites A framework for automatic labeling of log datasets from model-driven testbeds for hids evaluation.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models A framework for automatic labeling of log datasets from model-driven testbeds for hids evaluation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.117583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6b028644-3887-4ed7-b865-0c5f4963b517 · outbound

This paper cites Survey on intrusion detection system types.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Survey on intrusion detection system types

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.110860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 112e48e6-691b-4bae-9e2f-f0f6f137822a · outbound

This paper cites Design and implementation of network instruction detection system based on snort and ntop.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Design and implementation of network instruction detection system based on snort and ntop

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:39.040400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e2136309-42dc-4154-9638-3490ca95adc6 · outbound

This paper cites Improving intrusion detection system based on Snort rules for network probe attack detection.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Improving intrusion detection system based on Snort rules for network probe attack detection

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.949934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 01492768-f96c-4ca6-8175-b4d15992233c · outbound

This paper cites Large Language Models in Cybersecurity: State-of-the-Art.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Large Language Models in Cybersecurity: State-of-the-Art

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T15:28:38.284853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0da5a17-7597-40ca-a51a-3c317544c986 · outbound

This paper cites ChatGPT-information security overview.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models ChatGPT-information security overview

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.912628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6b665de1-d4b0-4989-b1d4-6c9bc4952845 · outbound

This paper cites Logprompt: Prompt engineering towards zero-shot and interpretable log analysis.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Logprompt: Prompt engineering towards zero-shot and interpretable log analysis

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.905225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 21ff4191-b9b6-4ff9-895e-bbedc3938dd2 · outbound

This paper cites Generating labeled training datasets towards unified network intrusion detection systems.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Generating labeled training datasets towards unified network intrusion detection systems

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.898191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 164387d8-38db-464b-afa0-bf5e2ac880e7 · outbound

This paper cites Ttpdrill: Automatic and accurate extraction of threat actions from unstructured text of CTI sources.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Ttpdrill: Automatic and accurate extraction of threat actions from unstructured text of CTI sources

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.891320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 124e8c5c-b1a7-4f23-9d26-bee14c4c8848 · outbound

This paper cites Automated Retrieval of ATT&CK Tactics and Techniques for Cyber Threat Reports.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Automated Retrieval of ATT&CK Tactics and Techniques for Cyber Threat Reports

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T15:28:38.306416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18c37c6f-285c-4676-ad9d-2ccf6bda8d6f · outbound

This paper cites Automatic mapping of vulnerability information to adversary techniques.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Automatic mapping of vulnerability information to adversary techniques

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.884766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 92a63f41-e693-44bc-bc67-d163bcc96f1b · outbound

This paper cites Extractor: Extracting attack behavior from threat reports.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Extractor: Extracting attack behavior from threat reports

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.876806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4136994c-aa77-4842-bf8c-67b4714436d2 · outbound

This paper cites TIM: threat context-enhanced TTP intelligence mining on unstructured threat data.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models TIM: threat context-enhanced TTP intelligence mining on unstructured threat data

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.869697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 68918549-0c9b-474c-8a3d-663d6c44100a · outbound

This paper cites Attackg: Constructing technique knowledge graph from cyber threat intelligence reports.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Attackg: Constructing technique knowledge graph from cyber threat intelligence reports

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.861118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2d163751-cbfc-4590-b9bc-1d98582ee17d · outbound

This paper cites TTPHunter: Automated extraction of actionable intelligence as TTPs from narrative threat reports.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models TTPHunter: Automated extraction of actionable intelligence as TTPs from narrative threat reports

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.777069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a9c1f482-712d-4dd7-a032-70dff910751e · outbound

This paper cites Securebert: A domain-specific language model for cybersecurity.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Securebert: A domain-specific language model for cybersecurity

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.694447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f7448b42-e8e0-4da2-a1b2-8d27ae52d6de · outbound

This paper cites Using natural language processing tools to infer adversary techniques and tactics under the mitre att&ck framework.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Using natural language processing tools to infer adversary techniques and tactics under the mitre att&ck framework

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.642292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f3e7960f-3f45-42e0-8d0f-a8c82dba1e9b · outbound

This paper cites Methods to employ zeek in detecting MITRE ATT&CK techniques.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Methods to employ zeek in detecting MITRE ATT&CK techniques

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.618486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f84f4fb1-0168-48af-a3aa-85d212ce8429 · outbound

This paper cites Design and development of automated threat hunting in industrial control systems.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Design and development of automated threat hunting in industrial control systems

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.610863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 093d7254-c10b-45ee-be6e-0d0aca2e5ff0 · outbound

This paper cites Towards a better labeling process for network security datasets.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Towards a better labeling process for network security datasets

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:28:38.540600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 368b08fc-27c4-4973-a999-f5badc4d1c89 · outbound

This paper cites Towards efficient labeling of network incident datasets using tcpreplay and snort.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Towards efficient labeling of network incident datasets using tcpreplay and snort

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.603124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ac71b587-a366-446b-bb8c-df3485022693 · outbound

This paper cites RADAR: A TTP-based Extensible, Explainable, and Effective System for Network Traffic Analysis and Malware Detection.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models RADAR: A TTP-based Extensible, Explainable, and Effective System for Network Traffic Analysis and Malware Detection

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:28:38.502337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c423e32b-afd5-4f54-b50b-63eba42ba527 · outbound

This paper cites Chatids: Advancing explainable cybersecurity using generative ai.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Chatids: Advancing explainable cybersecurity using generative ai

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.595326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a829d3c5-a529-40c7-a446-074661c5cb8c · outbound

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

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Labeling nids rules with mitre att &ck techniques using chatgpt

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:38.587029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7bc3c4c2-6f0c-4fcc-bfe9-d5826f43adcd · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Self-refine: Iterative refinement with self-feedback

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T15:28:38.340061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 595f98f8-aa5d-440d-9918-2ac9c35f9e0d · outbound

This paper cites Language models are unsupervised multitask learners.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Language models are unsupervised multitask learners

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T15:28:38.342316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e775b0c-8267-4f33-8056-ebb9c7fb67eb · outbound

This paper cites Language Models are Few-Shot Learners.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models Language Models are Few-Shot Learners

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T15:28:38.363690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c43b1df-29ab-4bcf-9d5e-550336d3a82c · outbound

This paper cites What Makes Good In-Context Examples for GPT-$3$?.

Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models What Makes Good In-Context Examples for GPT-$3$?

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T15:28:38.383642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation b3f2b95b-a826-420f-87b9-e5d9f5a0a281 · inbound

Cybersecurity Detection Classification with Reasoning-enabled Language Models cites this paper.

Cybersecurity Detection Classification with Reasoning-enabled Language Models Labeling NIDS Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-31T06:41:21.097683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.