Paper Citation Record · LEDGER

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification

As of 7 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2507.04372.

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

pith.paper-citation-record.v1
2507.04372 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:54:00.973510Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact2
verified fuzzy17
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 477f377f-b205-4148-b852-2d4801cc1138 · outbound

This paper cites Using ai and machine learning to predict and mitigate cybersecurity risks in critical infrastructure.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Using ai and machine learning to predict and mitigate cybersecurity risks in critical infrastructure

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:06.150371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8bdd891f-63dd-4e11-b514-ff93affab9b6 · outbound

This paper cites Novel feature extraction, selection and fusion for effective malware family classifica- tion.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Novel feature extraction, selection and fusion for effective malware family classifica- tion

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:05.900163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6ed68cb2-8952-4df0-ad77-b7b77ee614a4 · outbound

This paper cites Malbot-drl: Malware botnet detection using deep reinforcement learning in iot networks.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Malbot-drl: Malware botnet detection using deep reinforcement learning in iot networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:05.702363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 07610214-06c9-4d34-ba6c-739666a53abe · outbound

This paper cites Optimizing malware de- tectionandclassificationinreal-timeusinghy- brid deep learning approaches.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Optimizing malware de- tectionandclassificationinreal-timeusinghy- brid deep learning approaches

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:05.495056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation da3fa0e4-3848-43e5-9c47-09cf15479c78 · outbound

This paper cites an unresolved cited work.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:54:05.281445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 255bb8fd-fe41-4b82-be95-64f73046f81e · outbound

This paper cites A malware detection scheme based on mining format information.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification A malware detection scheme based on mining format information

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:05.021179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0fcb1969-9e70-468f-8f65-5976dca814e5 · outbound

This paper cites Adversarial environment re- inforcement learning algorithm for intrusion detection.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Adversarial environment re- inforcement learning algorithm for intrusion detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:04.864474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation abcfe5b5-38d0-4488-96ad-4ada5db3c30d · outbound

This paper cites Mal- ware detection & classification using machine learning.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Mal- ware detection & classification using machine learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:04.666896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b0fc6bc0-a9ac-4be1-8c92-9e4b2518b3e3 · outbound

This paper cites Feature selection for mal- ware detection based on reinforcement learn- ing.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Feature selection for mal- ware detection based on reinforcement learn- ing

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:04.435656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 347c1882-3133-49fe-883a-3097c2e0cfe4 · outbound

This paper cites Assess- ing the impact of packing on static machine learning-based malware detection and classifi- cation systems.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Assess- ing the impact of packing on static machine learning-based malware detection and classifi- cation systems

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:04.143385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 51756964-aff3-4601-987b-a4fe76c5cfec · outbound

This paper cites SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:59.522211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4ecc01b-a67b-4224-8c9e-e9e85fa132bd · outbound

This paper cites Enhancing malware detection with feature selection and scaling techniques using machine learning models.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Enhancing malware detection with feature selection and scaling techniques using machine learning models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:03.907093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8312eebc-406d-4b38-b342-6a44f8bfcb9f · outbound

This paper cites An efficient malware detection approach based on machine learn- ing feature influence techniques for resource- constrained devices.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification An efficient malware detection approach based on machine learn- ing feature influence techniques for resource- constrained devices

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:03.602086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa861db1-b5c9-42b1-a199-98daa9dc013b · outbound

This paper cites A Survey of Machine Learning Methods and Challenges for Windows Malware Classification.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification A Survey of Machine Learning Methods and Challenges for Windows Malware Classification

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:54:01.465142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6283d534-de7a-4dcb-a438-30b1029a31ad · outbound

This paper cites Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:54:01.212645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eb98a809-9a8b-466c-ac15-4c3df038fb0d · outbound

This paper cites Microsoft Malware Classification Challenge.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Microsoft Malware Classification Challenge

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T19:54:00.212092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94bef9cd-5a12-4a36-a9b8-1865898646a1 · outbound

This paper cites A state-of-the-art survey of malware detection approaches using data mining techniques.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification A state-of-the-art survey of malware detection approaches using data mining techniques

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:03.279842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8220066d-232b-4a21-9745-9a08f5eb8b02 · outbound

This paper cites Droidsieve: Fast and accurate classification of obfuscated android malware.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Droidsieve: Fast and accurate classification of obfuscated android malware

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:03.014357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a613d4f6-445f-404f-b003-e3e5187aa06f · outbound

This paper cites Static feature selection for iot malware detec- tion.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Static feature selection for iot malware detec- tion

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:02.652401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 67b44e2f-9f68-43ea-af64-645c09c7324d · outbound

This paper cites Droidrl: Feature selection for android malware detection with reinforcement learn- ing.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Droidrl: Feature selection for android malware detection with reinforcement learn- ing

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:02.366343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8bb2560a-a3cf-4eaf-ba59-26bb24f4fa3e · outbound

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

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification Bodmas: An open dataset for learning based temporal analysis of pe malware

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:02.040704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a163c7f-9ec8-4144-9475-a2ae8e3c01fa · outbound

This paper cites A novel image based approach for mobile android malware detection and classification.Knowledge-Based Systems, page 113855, 2025.

Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification A novel image based approach for mobile android malware detection and classification.Knowledge-Based Systems, page 113855, 2025

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:01.781931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.