Paper Citation Record · LEDGER

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning

As of 11 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2501.16453.

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

pith.paper-citation-record.v1
2501.16453 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:15:41.479627Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

39 of 39 outbound references displayed

verified exact0
verified fuzzy29
unresolved7
parse uncertain0
malformed identifier1
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6a622fec-0545-4629-8ebf-a1447c05e8e7 · outbound

This paper cites SANS Industrial Control Systems Security Blog 207 (2016) 16.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning SANS Industrial Control Systems Security Blog 207 (2016) 16

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:12.116211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1b48015d-9830-46fd-8018-d457e8bfb321 · outbound

This paper cites Electricity Information Sharing and Analysis Center (E-ISAC) 388(1-29), 3 (2016).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Electricity Information Sharing and Analysis Center (E-ISAC) 388(1-29), 3 (2016)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:12.101971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 87cd6a84-381d-4192-96e4-ff5df7ae1a88 · outbound

This paper cites http://www.statista.com/statistics/680953/global-malwarevolume/.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning http://www.statista.com/statistics/680953/global-malwarevolume/

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:12.090444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a988eb07-2968-47f7-9304-e2c774b88d4e · outbound

This paper cites In: 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning In: 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:12.078842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5a73da1d-7f0d-4795-ab4a-00c13fae2076 · outbound

This paper cites IEEE Communications Magazine 61(6), 28–34 (2023).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning IEEE Communications Magazine 61(6), 28–34 (2023)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:12.067491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 00f4d9cc-c6bb-4444-a2eb-b5ede7214a35 · outbound

This paper cites In: 2024 IEEE Power & Energy Society General Meeting (PESGM), pp.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning In: 2024 IEEE Power & Energy Society General Meeting (PESGM), pp

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T13:15:41.343119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d202847-5ca0-4e55-b4f7-685e95c709c2 · outbound

This paper cites In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), pp.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), pp

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:12.052461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 515cfe04-0adf-46aa-a9bb-bad2478d0fcb · outbound

This paper cites IEC Std 61850 (2013).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning IEC Std 61850 (2013)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:12.039122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 71c74758-e308-4f94-8b71-17fb82ed314c · outbound

This paper cites Journal of Electrical Systems and Information Technology 5(3), 468–483 (2018).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Journal of Electrical Systems and Information Technology 5(3), 468–483 (2018)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:12.027966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e3f5f49e-c9f9-481c-ba99-8ec7e1158d89 · outbound

This paper cites IEEE Transactions on Smart Grid 5(4), 1643–1653 (2014).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning IEEE Transactions on Smart Grid 5(4), 1643–1653 (2014)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:12.014861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0a3ebfbb-d022-438c-9129-c1489a24c398 · outbound

This paper cites ACM Transactions on Cyber-Physical Systems 7(2), 1–31 (2023).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning ACM Transactions on Cyber-Physical Systems 7(2), 1–31 (2023)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:12.002240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 38b951f5-d551-48a2-8313-28ca25bae43c · outbound

This paper cites Machine Learning Based Cyber System Restoration for IEC 61850 Based Digital Substations.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Machine Learning Based Cyber System Restoration for IEC 61850 Based Digital Substations

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T13:15:56.694246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 48db19b5-f9fa-4038-947c-a83b39eb2bc5 · outbound

This paper cites In: ISGT 2014, pp.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning In: ISGT 2014, pp

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.990151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 11331f17-a454-4f47-8e66-aa37ebe9f170 · outbound

This paper cites In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) (2020).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) (2020)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.978905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 18548adf-a88a-44d3-a695-4138104b6514 · outbound

This paper cites In: 2016 IEEE Power and Energy Society General Meeting (PESGM), pp.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning In: 2016 IEEE Power and Energy Society General Meeting (PESGM), pp

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.969140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 06a29a1b-118f-46a5-ade5-3b0b417850cb · outbound

This paper cites Energies 12(19), 3731 (2019).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Energies 12(19), 3731 (2019)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.960007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 26ad529a-7b0c-4dc9-9d55-1e1c0d5c490d · outbound

This paper cites Ieee Access 9, 56486– 56495 (2021).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Ieee Access 9, 56486– 56495 (2021)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.949864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c88300d0-d68d-4ee5-ab37-3b76f598e260 · outbound

This paper cites Advances in neural information processing systems 30 (2017).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Advances in neural information processing systems 30 (2017)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.937810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4227b195-db38-4f42-a277-cecd4d569e27 · outbound

This paper cites Language Models are Few-Shot Learners.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Language Models are Few-Shot Learners

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T13:15:41.392775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28e67287-6a70-463f-87e8-25dc8942912b · outbound

This paper cites In: 2024 IEEE International Confer- ence on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning In: 2024 IEEE International Confer- ence on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.926453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 63764e14-31d1-448f-98f3-651a3a9d7641 · outbound

This paper cites Journal of Big data 3, 1–40 (2016).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Journal of Big data 3, 1–40 (2016)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.915594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 69a2493f-c7af-4b0a-8b9a-5ae656010093 · outbound

This paper cites Generalized Out-of-Distribution Detection: A Survey.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Generalized Out-of-Distribution Detection: A Survey

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T13:15:41.409162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e63f44f-ea85-4afe-8047-dc4b00be1d1d · outbound

This paper cites Defense against Joint Poison and Evasion Attacks: A Case Study of DERMS.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Defense against Joint Poison and Evasion Attacks: A Case Study of DERMS

Reference 24

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T13:15:56.646882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1706abee-caa9-4eae-af5d-4d07a1ff6086 · outbound

This paper cites In: The Eleventh International Conference on Learning Representations (2022).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning In: The Eleventh International Conference on Learning Representations (2022)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.904426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e1b5ea87-3ed7-43b4-8157-8cc88771538f · outbound

This paper cites In: 2024 IEEE Interna- tional Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning In: 2024 IEEE Interna- tional Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp

Reference 26

Resolution: malformed identifier; no resolver link, observed 2026-08-10T13:15:41.421774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a1d5a24-d242-48ec-9701-e62f390b59c9 · outbound

This paper cites In: Matni, N., Morari, M., Pappas, G.J.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning In: Matni, N., Morari, M., Pappas, G.J

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.892385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2de743ef-482a-4b5d-8b0a-68e7b3a47332 · outbound

This paper cites National Science Review 5(1), 30–43 (2018).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning National Science Review 5(1), 30–43 (2018)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.881831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 36cc6d41-720f-4d25-861a-dc0e57322f88 · outbound

This paper cites IEEE Transactions on Dependable and Secure Computing (2023).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning IEEE Transactions on Dependable and Secure Computing (2023)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.870409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7f5d5947-6cb1-4688-aa93-3d761ad40414 · outbound

This paper cites OpenAI blog 1(8), 9 (2019).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning OpenAI blog 1(8), 9 (2019)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.859411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f42cf975-098e-4379-b7db-78ed224fce22 · outbound

This paper cites an unresolved cited work.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:16:11.847277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bde5b499-347d-420a-b3ec-2282633abe67 · outbound

This paper cites Advances in Neural Information Processing Systems 36 (2024).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Advances in Neural Information Processing Systems 36 (2024)

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.836151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 76393ee4-0d3a-427a-8b3a-a0bd3810b14b · outbound

This paper cites MetaICL: Learning to Learn In Context.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning MetaICL: Learning to Learn In Context

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T13:15:41.448866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b44d27c6-37d5-4b76-829c-b1bca1f74dd9 · outbound

This paper cites Diverse Demonstrations Improve In-context Compositional Generalization.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Diverse Demonstrations Improve In-context Compositional Generalization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T13:15:41.452386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9df85106-59ac-4533-bdba-2daabf890db1 · outbound

This paper cites an unresolved cited work.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:16:11.823935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 89e9c4ae-7f89-45a4-8755-e8881029d3f2 · outbound

This paper cites Advances in Neural Information Processing Systems 35, 18878– 18891 (2022).

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Advances in Neural Information Processing Systems 35, 18878– 18891 (2022)

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.812168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e2392020-5cf9-4900-b73a-f296e5fe26c8 · outbound

This paper cites Sensors 21(4), 1554 (2021) Nomenclature Model Parameters and Dimensions: I Number of training samples used to train the transformer model.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Sensors 21(4), 1554 (2021) Nomenclature Model Parameters and Dimensions: I Number of training samples used to train the transformer model

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.799356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c84929c8-551c-4a40-a912-ff5e0b432c45 · outbound

This paper cites Each prediction depends only on past observa- tions, naturally aligning with the temporal ordering of packet streams in digital substations.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning Each prediction depends only on past observa- tions, naturally aligning with the temporal ordering of packet streams in digital substations

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.784095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c472ec7b-7fbf-4257-b3e7-f1c29981a777 · outbound

This paper cites This facilitates handling variable-length input sequences without architectural changes, crucial for evolving network traffic patterns.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning This facilitates handling variable-length input sequences without architectural changes, crucial for evolving network traffic patterns

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.769512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 18abbcb7-d050-484c-b77c-4015f5f0fea7 · outbound

This paper cites By omitting an encoder stage, the model can efficiently process real-time data while preserving strong pattern- recognition capabilities.

Detecting Zero-Day Attacks in Digital Substations via In-Context Learning By omitting an encoder stage, the model can efficiently process real-time data while preserving strong pattern- recognition capabilities

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:16:11.756353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.