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Paper Citation Record · LEDGER

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2506.04454.

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

pith.paper-citation-record.v1
2506.04454 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:46:22.462785Z

measured 34 of 34 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 2026-05-18T18:04:09.528381Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T18:06:43.032769Z

Reference resolution

33 of 33 outbound references displayed

  • verified exact1
  • verified fuzzy21
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cdfff4b9-01f0-4ba9-97ea-6138e6087f2e · outbound

This paper cites An intelligent tree-based intrusion detection model for cyber security.Journal of Network and Systems Management, 29(2):20, 2021.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning An intelligent tree-based intrusion detection model for cyber security.Journal of Network and Systems Management, 29(2):20, 2021

Reference 1

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e05ac072-56bc-4b52-85f2-a141e3b8d751 · outbound

This paper cites an unresolved cited work.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Unresolved cited work

Reference 2

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raw_fallback, observed 2026-08-07T10:46:22.773076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation d3b9615a-d1b7-40b9-9d9b-e3337497f174 · outbound

This paper cites an unresolved cited work.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Unresolved cited work

Reference 3

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unresolved
raw_fallback, observed 2026-08-07T10:46:22.763925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.358132Z digest=sha256:5bb49c7633a8986ea6743ffa7728dcaac21e07360726b5436689ba9aa2e82b3e

Observation 2f1db8cc-1525-4444-9572-a734c82a1819 · outbound

This paper cites Application of image processing and transfer learning for the detection of rust disease.Scientific Reports, 13(1):5133, 2023.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Application of image processing and transfer learning for the detection of rust disease.Scientific Reports, 13(1):5133, 2023

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.361421Z digest=sha256:87ff5a7e7dabe7a7b8a0a363a401cabea97a2c8dae930e2c88855d55b152dd8a

Observation 61d398b0-b58c-4579-8ea8-81e4b754a187 · outbound

This paper cites an unresolved cited work.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Unresolved cited work

Reference 5

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unresolved
raw_fallback, observed 2026-08-07T10:46:22.746114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.365143Z digest=sha256:a838f56ca66675b7c1e7ad4a256e53ba2f4be3a500bb5e905cde401ce1259b31

Observation 9c2c6e6a-6503-40ec-91a9-d6581241142f · outbound

This paper cites Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition

Reference 6

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unresolved
no resolver link, observed 2026-08-07T10:46:22.369091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:46:22.369091Z digest=sha256:b3de4574a9ed9807501244375be944dba535ff20d9e59193de5ba69c8365acf2

Observation 4683aa95-7874-4495-b321-6bbfdc9e1111 · outbound

This paper cites Transfer learning for medical image classification: a literature review.BMC medical imaging, 22(1):69, 2022.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Transfer learning for medical image classification: a literature review.BMC medical imaging, 22(1):69, 2022

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.372922Z digest=sha256:7c091ec9adf6835899732e4b140d205607a96dc2f24640a789b845827d67fa40

Observation b86d1ca9-37bc-4734-8180-6498a9033378 · outbound

This paper cites An intrusion-detection model.IEEE Transactions on software engineering, SE-13(2):222– 232, 1987.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning An intrusion-detection model.IEEE Transactions on software engineering, SE-13(2):222– 232, 1987

Reference 8

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verified fuzzy
raw_fallback, observed 2026-08-07T10:46:22.728414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.375961Z digest=sha256:15eb04011c62f9b652bc25cebab6229b18f96b8ee08037dc407d66dfb61e08cd

Observation 42f637a1-e1d8-40e3-8b8d-279db925c077 · outbound

This paper cites Generalized out-of-distribution detection: A survey.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Generalized out-of-distribution detection: A survey

Reference 9

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.379002Z digest=sha256:db3c0ed2060bd904a6bcf9b7c80ceecb666789098a7ee33b985489a32dd4a5e3

Observation 554d5731-cfa3-4a50-bf4d-a86280d8c252 · outbound

This paper cites Pavlik, and Nathaniel D.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Pavlik, and Nathaniel D

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.382089Z digest=sha256:34f5eef073ea490edbd1e23964944cabe46645f695de7f5cc98c812b4f2a6f23

Observation f6e9e4a5-c67a-45a2-b850-03fb72705bb7 · outbound

This paper cites Unsw-nb15: A comprehensive data set for network intrusion detection systems (unsw-nb15 network data set).

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Unsw-nb15: A comprehensive data set for network intrusion detection systems (unsw-nb15 network data set)

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.385770Z digest=sha256:757e94c96f7ec195cf43c073f0f7e0d7ff53184cae547248160537e120d77fd6

Observation 921379c7-9d3d-4971-a34b-e3e7be744d24 · outbound

This paper cites Toward generating a new intrusion detection dataset and intrusion traffic characterization.ICISSp, 1:108–116, 2018.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Toward generating a new intrusion detection dataset and intrusion traffic characterization.ICISSp, 1:108–116, 2018

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-07T10:46:22.691257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.388827Z digest=sha256:009bee33e8c83d6e39ad6e27587475dda871d16c7f2b449ef496e8f720cc1b99

Observation d0126981-c52b-4610-91dc-f96610c6e970 · outbound

This paper cites Aci iot network traffic dataset 2023, Apr 2024.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Aci iot network traffic dataset 2023, Apr 2024

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-07T10:46:22.682719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.391866Z digest=sha256:20c2e977d71e438d9362298125abfc9e640947e6b697cb5caac83f87f347ffe0

Observation a173ca98-0a5f-44a0-bc70-650b6bdd79df · outbound

This paper cites A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI

Reference 14

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no resolver link, observed 2026-08-07T10:46:22.395273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:46:22.395273Z digest=sha256:246e46303e5af67a0a8507b838133a0942a4d426e812e8eeca0e6ee21c35f8f3

Observation 94f5a1e6-0573-4b0f-a634-62957dcbd085 · outbound

This paper cites Surgical Fine-Tuning Improves Adaptation to Distribution Shifts.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Surgical Fine-Tuning Improves Adaptation to Distribution Shifts

Reference 15

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unresolved
no resolver link, observed 2026-08-07T10:46:22.398872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:46:22.398872Z digest=sha256:5f830986754a149561a0dc538c5b47caa52bb9e2a6864f13ac04da4c57e720c0

Observation 86ef9fd1-a7f4-4e37-861e-0537a2db17bc · outbound

This paper cites Transtailor: Pruning the pre-trained model for improved transfer learning.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Transtailor: Pruning the pre-trained model for improved transfer learning

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-07T10:46:22.673905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.402373Z digest=sha256:8b4d1e4b46eb23f8e401a72123d49ebfa6ad5683ece9473a7662799d0b939c99

Observation 224525f6-3e22-4e62-bf7f-cbc36f068f36 · outbound

This paper cites Spottune: Transfer learning through adaptive fine-tuning.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Spottune: Transfer learning through adaptive fine-tuning

Reference 17

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verified fuzzy
raw_fallback, observed 2026-08-07T10:46:22.664825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.405604Z digest=sha256:86a62eaacb1cdb4a140287997338350734e7d81b5b6e1ee1c3356ea558b51a3f

Observation a17d3b1e-1bf8-4f53-9cd3-daf12c01a8ac · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 19

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no resolver link, observed 2026-08-07T10:46:22.416174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:46:22.416174Z digest=sha256:c8fa6615e75bde662609ecc58389bee8e5ebb23f9243045ba97a5c0b10cb18b3

Observation aba589ea-ed53-48d0-bde2-49364ab6395e · outbound

This paper cites Wong, Alexander M.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Wong, Alexander M

Reference 20

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.420054Z digest=sha256:2e188a759564789689151a13fe61e554edf58ea42f880665d21d5865db6d53e5

Observation d753d957-7a75-4c14-95e7-61ca6b59f4d0 · outbound

This paper cites Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI

Reference 21

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local_arxiv, observed 2026-08-07T10:46:22.501289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.423071Z digest=sha256:6ab8138a9cfa0f99c6a09020723a1196e12c074f1f2a23050d7554f90d102194

Observation a1023e6d-72bd-4882-bff2-1934261b7696 · outbound

This paper cites Confidence scoring using whitebox meta-models with linear classifier probes.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Confidence scoring using whitebox meta-models with linear classifier probes

Reference 22

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verified fuzzy
raw_fallback, observed 2026-08-07T10:46:22.646159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.426326Z digest=sha256:433af3f88c067c4df1571bc31ae99b8cb8039f73477c94d3da368f979146a1ca

Observation 51f2d344-2487-4279-b095-32549bbfef6d · outbound

This paper cites Lundberg and Su-In Lee.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Lundberg and Su-In Lee

Reference 23

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verified fuzzy
raw_fallback, observed 2026-08-07T10:46:22.636784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.429954Z digest=sha256:c810184bd60e5b20198bff1e5b3eba30d1c87a337163f82eae45b80f0e7a448b

Observation 1eea3d16-f570-4cb8-8377-eab915e325cc · outbound

This paper cites Ross Quinlan.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Ross Quinlan

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-07T10:46:22.626029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.433348Z digest=sha256:d5cea63dccf07916aeb3f0ddd486d1e3c5927d159786c610bef5253461b7bf7f

Observation dd2cdf5f-6159-4946-93e2-41e7d955fe95 · outbound

This paper cites Unsupervised deep embedding for clustering analysis.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Unsupervised deep embedding for clustering analysis

Reference 25

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raw_fallback, observed 2026-08-07T10:46:22.617046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.436554Z digest=sha256:1f4a7af2f1090e50904f3d727278e9abe183853653bb2bfcda179404c1c7a208

Observation 9a84b8ad-feae-49be-9bf3-661217eb91c1 · outbound

This paper cites Xgboost: A scalable tree boosting system.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Xgboost: A scalable tree boosting system

Reference 26

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raw_fallback, observed 2026-08-07T10:46:22.607406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.439813Z digest=sha256:e5bb382018764938d3a0aaee4a5241249db4d31d1020c462f9ee4754a63ae892

Observation e96355de-0da0-4f12-9d85-72bc2a77e2e7 · outbound

This paper cites Quantifying information flow using min-entropy.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Quantifying information flow using min-entropy

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-07T10:46:22.598208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.443144Z digest=sha256:58296db9efa192028acd14fb64e598d7435f60a9ed88679933bbed4670a5e6bf

Observation 290a6fc2-7743-470d-88bf-2371d0dd4740 · outbound

This paper cites Single-model uncertainties for deep learning.Advances in Neural Information Processing Systems, 32:6415–6425, 2019.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Single-model uncertainties for deep learning.Advances in Neural Information Processing Systems, 32:6415–6425, 2019

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-07T10:46:22.588761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.446863Z digest=sha256:eb99571d14b857727c9798cd5111c42c5d402ac9770a31d219dacc368a22f7b0

Observation 61c19d39-b207-4f29-a844-2f9e2f03b04d · outbound

This paper cites Lundberg, Gabriel G.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Lundberg, Gabriel G

Reference 29

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no resolver link, observed 2026-08-07T10:46:22.449901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:46:22.449901Z digest=sha256:934a0bc367e7872e8534d7dc2cab0cee4fbfcb5521ff97d7ac584d4d3f6210c9

Observation 067caca2-7d9d-4f7b-9238-1e423b6ddccd · outbound

This paper cites From local explanations to global understanding with explainable ai for trees.Nature machine intelligence, 2(1):56–67, 2020.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning From local explanations to global understanding with explainable ai for trees.Nature machine intelligence, 2(1):56–67, 2020

Reference 30

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unresolved
no resolver link, observed 2026-08-07T10:46:22.453573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:46:22.453573Z digest=sha256:918d53a35bc4d0dd88c3c4ecf21a4aadc4852fde53ce62867ef71788bd9384dd

Observation 7c48bc1b-3a91-46a4-a1dd-e21443b411dc · outbound

This paper cites The use of the area under the roc curve in the evaluation of machine learning algorithms.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning The use of the area under the roc curve in the evaluation of machine learning algorithms

Reference 31

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raw_fallback, observed 2026-08-07T10:46:22.568286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.456852Z digest=sha256:2b03a6db7891868e9c5c5f0befac0be69d657caf52ad2d8bdc7f7f62ee22c173

Observation 71e71c9f-1061-46e8-be71-2a6a8f0adcd3 · outbound

This paper cites Bastian, Daniel Clouse, Bradford Kline, and Susmit Jha.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning Bastian, Daniel Clouse, Bradford Kline, and Susmit Jha

Reference 32

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raw_fallback, observed 2026-08-07T10:46:22.558502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.459800Z digest=sha256:e9923fb622b3bcc91473ded596ba1037f866c2c7b2e814c49c116552c0424151

Observation 101ba47d-311f-45e9-bd23-63be135c6cbf · outbound

This paper cites ACI IoT Network Traffic Dataset 2023.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning ACI IoT Network Traffic Dataset 2023

Reference 33

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no resolver link, observed 2026-08-07T10:46:22.462785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:46:22.462785Z digest=sha256:cabd990036724d39e310c791e2fc8e2c2ed40487a58db7ea5abaa16e62af124e

Observation 9f1c03c4-de75-49e5-afbd-c6233a8a0f7b · outbound

This paper cites URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks.

Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks

Reference 2020

Resolution
verified exact
local_arxiv, observed 2026-08-07T10:46:22.522770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T10:46:22.412676Z digest=sha256:7655acaae9f7dd06d5057a088d9a8c9f4ba30d3b11ee9d4191aae03eaf7e5789

Pith citing papers

Observation dfd2f5d3-cc20-469b-8652-1596ff94b221 · inbound

Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities cites this paper.

Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning

Reference 59

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arxiv_id, observed 2026-05-18T18:06:43.035734Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T18:04:09.528381Z digest=sha256:6be8105166d86a7494d24837ed9072e5a7cc8fcc88b1cd10c46e9e64c8fd9ed9