Paper Citation Record · LEDGER

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability

As of 16 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2505.01328.

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

pith.paper-citation-record.v1
2505.01328 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:26:16.688893Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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 2026-05-18T18:06:42.973254Z

Reference resolution

27 of 27 outbound references displayed

verified exact0
verified fuzzy17
unresolved9
parse uncertain1
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 50ca185d-4dc7-44b1-8ea1-1a2c9e4d4b92 · outbound

This paper cites Insomnia: Towards concept-drift robustness in network intrusion detection,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Insomnia: Towards concept-drift robustness in network intrusion detection,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:17.185272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 427d3c7f-c2f5-43d8-aed1-c9dc3a981fd2 · outbound

This paper cites Adversarial machine learning in network intrusion detection systems,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Adversarial machine learning in network intrusion detection systems,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:17.164872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 45326e0d-672e-49c1-b95b-81e1c5b0659f · outbound

This paper cites Intriguing properties of neural networks.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Intriguing properties of neural networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T04:26:16.565000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78f348ec-01c1-4e42-a6cb-c001ee49d5a5 · outbound

This paper cites Adver- sarial attacks on machine learning cybersecurity defences in industrial control systems,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Adver- sarial attacks on machine learning cybersecurity defences in industrial control systems,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T04:26:16.570204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b41263e2-aa12-4dc8-bd86-8bccf613868a · outbound

This paper cites Ebsnn: Ex- tended byte segment neural network for network traffic classification,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Ebsnn: Ex- tended byte segment neural network for network traffic classification,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:17.132841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e03dbb94-8fd8-4c04-8824-e4b53b7fce31 · outbound

This paper cites Hpac-ids: A hierarchical packet attention convolution for intrusion detection system,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Hpac-ids: A hierarchical packet attention convolution for intrusion detection system,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:17.111614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5a0477ba-c94b-47f9-aaa6-ce4f36fbf3d2 · outbound

This paper cites Addressing adversarial attacks against security systems based on machine learning,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Addressing adversarial attacks against security systems based on machine learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:17.092304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0608e5ba-7996-4db1-a1e9-2b66f72c6d53 · outbound

This paper cites Adversarial machine learning applied to intrusion and malware scenarios: a systematic review,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Adversarial machine learning applied to intrusion and malware scenarios: a systematic review,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:17.062104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 86c11946-787e-4d4e-b20d-3b11db294cad · outbound

This paper cites Adversarial machine learning for cyber security,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Adversarial machine learning for cyber security,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:17.044349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 650a6425-b002-4603-a35d-4bf19e216eb6 · outbound

This paper cites Adversarial examples against the deep learning based network intrusion detection systems,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Adversarial examples against the deep learning based network intrusion detection systems,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:17.025337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b8d67250-cc47-453f-bc1d-0b1576a5d8ac · outbound

This paper cites Adversarial deep learning against intrusion detection clas- sifiers,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Adversarial deep learning against intrusion detection clas- sifiers,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:17.008180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3a4179e7-31cb-4e1f-a220-75ec87f754e3 · outbound

This paper cites IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T04:26:16.616073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e2b4611-a525-4e75-abae-b8b2ea65fac8 · outbound

This paper cites Nids-cbad: Constraint-based adversarial detection in network intrusion detection systems,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Nids-cbad: Constraint-based adversarial detection in network intrusion detection systems,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:16.991408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b7d6702f-9e99-47a5-b9ac-297d2f212b94 · outbound

This paper cites A systematic study of adversarial attacks against ml-based network intrusion detection systems in iot environ- ments,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability A systematic study of adversarial attacks against ml-based network intrusion detection systems in iot environ- ments,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:16.973271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 21fddc6c-e2e1-4e48-95f2-e4ec185a9e6f · outbound

This paper cites On the feasibility of adversarial machine learning in malware and network intrusion detection,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability On the feasibility of adversarial machine learning in malware and network intrusion detection,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:16.953592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 21d68155-9bf9-495f-ae80-444f35e38b94 · outbound

This paper cites Towards evaluation of nidss in adversarial setting,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Towards evaluation of nidss in adversarial setting,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:16.936164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 577eacda-5c95-4e31-8c8b-167634dedb8a · outbound

This paper cites Subverting network in- trusion detection: Crafting adversarial examples accounting for domain- specific constraints,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Subverting network in- trusion detection: Crafting adversarial examples accounting for domain- specific constraints,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:16.918990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9daa8586-1f03-408b-a553-b46610307204 · outbound

This paper cites Adversarial Examples in Constrained Domains.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Adversarial Examples in Constrained Domains

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T04:26:16.646347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation def17aac-be75-4669-bcd4-04e171169560 · outbound

This paper cites A study on nsl-kdd dataset for intrusion detection system based on classification algorithms,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability A study on nsl-kdd dataset for intrusion detection system based on classification algorithms,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:16.900878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ed511eb9-506d-487e-a103-592afacf94ef · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Explaining and Harnessing Adversarial Examples

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T04:26:16.657034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4d1a833-f266-44bc-af73-115eaaebe718 · outbound

This paper cites Towards evaluating the robustness of neural networks,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Towards evaluating the robustness of neural networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:16.883335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 85a4717c-7605-4fa1-aab6-0313ca0e08d9 · outbound

This paper cites The limitations of deep learning in adversarial settings,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability The limitations of deep learning in adversarial settings,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T04:26:16.667536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bebb873-55b6-43ea-a78e-f3e33ea18aed · outbound

This paper cites Deepfool: a sim- ple and accurate method to fool deep neural networks,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Deepfool: a sim- ple and accurate method to fool deep neural networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:26:16.852998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f666942c-fa63-480a-b02b-50f37028441e · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T04:26:16.678132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2f11497-1be6-470f-b6a7-eec877c92303 · outbound

This paper cites Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T04:26:16.683562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 140a2449-74d3-44b3-8d86-3fd81f3a7430 · outbound

This paper cites Adversarial Machine Learning at Scale.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Adversarial Machine Learning at Scale

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T04:26:16.688893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c331ebd3-7c6a-40a5-890d-438f8d032d8b · outbound

This paper cites an unresolved cited work.

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability Unresolved cited work

Reference 900

Resolution: parse uncertain; no resolver link, observed 2026-08-16T04:26:16.591246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 39e20929-5b02-4110-878e-47d6268e880c · 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 Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:06:42.976385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.