Paper Citation Record · LEDGER

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection

As of 8 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2602.09634.

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

pith.paper-citation-record.v1
2602.09634 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T02:46:19.766863Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T14:29:04.708853Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

verified exact0
verified fuzzy0
unresolved26
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4b7e3966-5fe0-4509-8e80-d869cc4b0f7a · outbound

This paper cites A review on large language models: Architectures, applications, taxonomies, open issues and challenges,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection A review on large language models: Architectures, applications, taxonomies, open issues and challenges,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:16.626284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8ea637a-25bd-4788-a0e3-95f2f1df33c2 · outbound

This paper cites Enhancing malware detection with fea- ture selection and scaling techniques using machine learning models,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Enhancing malware detection with fea- ture selection and scaling techniques using machine learning models,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:16.769950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7c51278-4cd2-4de9-980c-dd5601ff8bab · outbound

This paper cites The impact of feature selection on malware classification using chi-square and machine learning,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection The impact of feature selection on malware classification using chi-square and machine learning,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:16.934679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e66888c0-3d49-4a2a-aef8-f17ef35fbd9b · outbound

This paper cites Feature selection for malware detection based on reinforcement learning,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Feature selection for malware detection based on reinforcement learning,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:17.099767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37610cb1-3c90-4654-aafc-52df60edc2f3 · outbound

This paper cites Analysis and comparison of feature selection methods towards performance and stability,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Analysis and comparison of feature selection methods towards performance and stability,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:17.237998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13c69516-3ce3-436a-917a-70265b82bf70 · outbound

This paper cites A review of feature selection methods for actual evapotranspiration prediction,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection A review of feature selection methods for actual evapotranspiration prediction,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:17.340990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82d71388-5492-4dd7-b190-082edb86b7e5 · outbound

This paper cites Stability of feature selection algorithm: A review,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Stability of feature selection algorithm: A review,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:17.457647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12f5bebc-2920-4e63-986b-ad9c84fa3249 · outbound

This paper cites LLM-Select: Feature Selection with Large Language Models.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection LLM-Select: Feature Selection with Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:17.574574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92f85ba2-8fed-4cfc-ab36-838a154134a4 · outbound

This paper cites Knowledge-driven feature selection and engineering for genotype data with large language models,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Knowledge-driven feature selection and engineering for genotype data with large language models,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:17.742701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f650aa5c-0c0a-4f65-bb2a-62957aa20628 · outbound

This paper cites LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:17.875218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48581438-e7b5-486c-87f3-45294bafd1b6 · outbound

This paper cites EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:17.972905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebc6317e-1960-40e5-b5cc-f0c635a45e7f · outbound

This paper cites Bodmas: An open dataset for learning based temporal analysis of pe malware,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Bodmas: An open dataset for learning based temporal analysis of pe malware,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:18.089402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5230b9a9-9963-4b7e-ad30-4c8444a5c1b8 · outbound

This paper cites Stacking llm models’ predictions for feature selection in anomaly classification,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Stacking llm models’ predictions for feature selection in anomaly classification,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:18.168193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed3f9b69-4fe6-41c7-afa3-d7e4319d82f4 · outbound

This paper cites Initial-llm: A large language model-guided metaheuristic framework for enhanced feature selection in clinical decision support systems,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Initial-llm: A large language model-guided metaheuristic framework for enhanced feature selection in clinical decision support systems,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:18.235990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bb9a778-79e3-4350-9fec-c07a555f2c5e · outbound

This paper cites A review of feature selection methods for machine learning-based disease risk prediction,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection A review of feature selection methods for machine learning-based disease risk prediction,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:18.333701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa5c8750-b4b9-4dda-a4f6-d0eda5973dc9 · outbound

This paper cites Malware analysis and detection using machine learning algorithms,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Malware analysis and detection using machine learning algorithms,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:18.419577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1bb191e-ba74-4386-91d6-ef7d0398e91b · outbound

This paper cites An introduction to variable and feature selection,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection An introduction to variable and feature selection,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:18.537607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61e4119d-5e47-41cb-9197-4b6209967b90 · outbound

This paper cites Chi2: Feature selection and discretization of numeric attributes,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Chi2: Feature selection and discretization of numeric attributes,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:18.632849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a861fa33-3b9a-404e-8d08-2b557df6ff0d · outbound

This paper cites Optimal ensemble learning based on distinctive feature selection by univariate anova-f statistics for ids,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Optimal ensemble learning based on distinctive feature selection by univariate anova-f statistics for ids,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:18.798066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee3d551f-6f35-4ba1-9746-53a4743d1358 · outbound

This paper cites Feature selection based on mu- tual information criteria of max-dependency, max-relevance, and min- redundancy,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Feature selection based on mu- tual information criteria of max-dependency, max-relevance, and min- redundancy,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:18.976551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb191f5a-946e-4e58-9be6-6515403a1b54 · outbound

This paper cites Correlation-based feature selection for machine learning,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Correlation-based feature selection for machine learning,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:19.174914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ca348cc-d979-487c-a5d2-5ff1f9f70b9b · outbound

This paper cites Random forests,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Random forests,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:19.320620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb90d68e-da21-40cb-adf8-7ad29741c97d · outbound

This paper cites Extremely randomized trees,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Extremely randomized trees,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:19.491856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd806934-1cde-4529-8b39-6cde88a9e82a · outbound

This paper cites Wrappers for feature subset selection,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection Wrappers for feature subset selection,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:19.621680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 346bcc27-09fe-400f-8ff8-fd479668d4ea · outbound

This paper cites A survey on machine learning-based malware detection in executable files,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection A survey on machine learning-based malware detection in executable files,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:19.680705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a07a2098-5dfb-4227-a054-7257037e2656 · outbound

This paper cites A comprehensive review on malware detection approaches,.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection A comprehensive review on malware detection approaches,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:19.766863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation f0fbfb2e-db26-45bc-9d11-313cc3a0db87 · inbound

SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification cites this paper.

SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T14:29:04.708853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.