Paper Citation Record · LEDGER

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection

As of 5 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2605.04407.

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

pith.paper-citation-record.v1
2605.04407 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T18:17:11.653430Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

25 of 25 outbound references displayed

verified exact0
verified fuzzy21
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 69cadde9-5bc7-4531-acb9-17ff2b3b9875 · outbound

This paper cites an unresolved cited work.

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-05-26T05:21:49.002575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 214ac0b7-ce45-41a6-a27d-4ef41c3ba072 · outbound

This paper cites In: 2020 International Confer- ence on Data Science, Artificial Intelligence, and Business Analytics (DATABIA).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection In: 2020 International Confer- ence on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.998979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 565ea043-8cb6-4747-8190-077e4961b059 · outbound

This paper cites SN Computer Science5(8), 1028 (2024).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection SN Computer Science5(8), 1028 (2024)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:49.005976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 28573949-1f0e-4ea2-89e2-8d06ebb053ab · outbound

This paper cites Mesopotamian Journal of CyberSecurity2021, 1–4 (2021).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection Mesopotamian Journal of CyberSecurity2021, 1–4 (2021)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:49.013343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 457365a8-4ef1-48bc-a5a7-a893c11438bf · outbound

This paper cites Systems Science & Control Engineering12(1), 2321381 (2024).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection Systems Science & Control Engineering12(1), 2321381 (2024)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.987361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation afffa6b5-4d0d-4a25-9017-d7831d6844c3 · outbound

This paper cites In: 2017 IEEE 15th inter- national symposium on intelligent systems and informatics (SISY).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection In: 2017 IEEE 15th inter- national symposium on intelligent systems and informatics (SISY)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.990921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0acaed31-4daa-45dc-9b6b-912d3d8d96a5 · outbound

This paper cites an unresolved cited work.

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-05-26T05:21:48.968211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a72cbb8f-6ee3-4cb6-abd3-8739bf90485e · outbound

This paper cites In: International Conference on Digital Technologies and Applications.

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection In: International Conference on Digital Technologies and Applications

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.964477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 66a5e3b6-cec6-4b1e-8db2-db837f6b5289 · outbound

This paper cites Scientific Reports (2026).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection Scientific Reports (2026)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.980183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 977dbff1-cf69-4c83-9dd4-134e773c17c6 · outbound

This paper cites IEEE access9, 142206–142217 (2021).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection IEEE access9, 142206–142217 (2021)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.994588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dde6a3e8-3417-4ee8-904e-e9db7155ba2f · outbound

This paper cites IEEE Systems Journal15(2), 1717–1731 (2020).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection IEEE Systems Journal15(2), 1717–1731 (2020)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.945000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8d17b804-2dcd-45c4-a4b2-579721c3b0a0 · outbound

This paper cites In: 2017 IEEE 26th international symposium on industrial electronics (ISIE).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection In: 2017 IEEE 26th international symposium on industrial electronics (ISIE)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.941220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a2fdb14f-514e-4a81-9e6a-62a9c88142c6 · outbound

This paper cites International Journal of Engineering Applied Sciences and Technology4(6), 2455–2143 (2019) Generalisation Capability of ML for Intrusion Detection 13.

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection International Journal of Engineering Applied Sciences and Technology4(6), 2455–2143 (2019) Generalisation Capability of ML for Intrusion Detection 13

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.949432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cdb2555d-06d1-44dd-a510-282b802015ab · outbound

This paper cites an unresolved cited work.

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-05-26T05:21:48.953188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 27c8f196-f421-473a-927c-57331ec70b00 · outbound

This paper cites BIN: Bulletin of Informatics2(2), 248–61 (2024).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection BIN: Bulletin of Informatics2(2), 248–61 (2024)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.956984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 36870be0-d7b3-4a33-b9d3-ee6ac1552baf · outbound

This paper cites In: International conference on neural information processing.

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection In: International conference on neural information processing

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.960764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cdda83f0-85b0-448f-98d6-c6e3d9603576 · outbound

This paper cites Expert Systems with Applications124, 196–208 (2019).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection Expert Systems with Applications124, 196–208 (2019)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.928946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2ae930bf-405c-415d-8328-7807153c227d · outbound

This paper cites Computers & Security148, 104175 (2025).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection Computers & Security148, 104175 (2025)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.933160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a76d834d-697b-48d2-a3b1-6fef77eded5d · outbound

This paper cites IEEE access9, 22351–22370 (2021).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection IEEE access9, 22351–22370 (2021)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.971870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 604ccc71-55c4-4ec1-99be-7b2a70f11f44 · outbound

This paper cites an unresolved cited work.

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-05-26T05:21:48.976646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0266f644-d11e-41a5-9a03-3f5dc2955ea4 · outbound

This paper cites In: 2015 military communications and information systems conference (MilCIS).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection In: 2015 military communications and information systems conference (MilCIS)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.922810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a83a57aa-6460-4cf3-81fb-0747c31b36e6 · outbound

This paper cites Information Security Journal: A Global Perspective25(1-3), 18–31 (2016).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection Information Security Journal: A Global Perspective25(1-3), 18–31 (2016)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.937329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 57eab71b-c122-4e55-8ed8-7424754aab4c · outbound

This paper cites In: 2022 International Conference on Advances in Computing, Communication and Materials (ICACCM).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection In: 2022 International Conference on Advances in Computing, Communication and Materials (ICACCM)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.913200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 74ea6828-6b4d-4e83-a112-1d2787f2de6a · outbound

This paper cites Procedia Computer Science171, 1251–1260 (2020).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection Procedia Computer Science171, 1251–1260 (2020)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.983625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1d27aae4-6671-4739-936e-97c39ffd6362 · outbound

This paper cites In: IEEE INFOCOM 2018-IEEE conference on computer com- munications workshops (INFOCOM WKSHPS).

Assessing Generalisation Capability of Machine Learning Models for Intrusion Detection In: IEEE INFOCOM 2018-IEEE conference on computer com- munications workshops (INFOCOM WKSHPS)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:21:48.918712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

No inbound Pith citation observations are available.