Paper Citation Record · LEDGER

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining

As of 10 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2608.05605.

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

pith.paper-citation-record.v1
2608.05605 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:43:26.691692Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

28 of 28 outbound references displayed

verified exact0
verified fuzzy26
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e426dea6-96c6-41fa-9d03-dc7a6d7064cd · outbound

This paper cites SDN for End-to-End Networked Science at the Exascale (SENSE),.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining SDN for End-to-End Networked Science at the Exascale (SENSE),

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.474745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a234d5c0-46c7-468a-afe0-79b92df02bf0 · outbound

This paper cites Ai-driven multilayered cybersecurity intelli- gence framework for critical infrastructure protection,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Ai-driven multilayered cybersecurity intelli- gence framework for critical infrastructure protection,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.425408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d96ff964-4bbd-4fab-8364-1da124b366cb · outbound

This paper cites The Science DMZ: A network design pattern for data-intensive science,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining The Science DMZ: A network design pattern for data-intensive science,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.400619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f529624d-f6e3-496d-a796-66397ef12ba4 · outbound

This paper cites Challenges with Collecting, Anonymizing, Sharing and Using High- Speed Network-Traffic Data (White Paper),.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Challenges with Collecting, Anonymizing, Sharing and Using High- Speed Network-Traffic Data (White Paper),

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.391472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8081bb42-6397-442c-a882-254e6f70ff05 · outbound

This paper cites A fluid-flow characterization of Internet1 and Internet2 traffic,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining A fluid-flow characterization of Internet1 and Internet2 traffic,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.381132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0630d62e-b589-4d0a-9efd-db72a3f443fe · outbound

This paper cites Understanding flows in high-speed scientific networks: A Netflow data study,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Understanding flows in high-speed scientific networks: A Netflow data study,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.371788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7e5897e1-2f03-436f-8579-712aa4239784 · outbound

This paper cites A signal analysis of network traffic anomalies,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining A signal analysis of network traffic anomalies,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.362371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7fa49b53-49b5-4328-8aba-ec676c521ff3 · outbound

This paper cites Understanding Data Movement Patterns in HPC: A NERSC Case Study,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Understanding Data Movement Patterns in HPC: A NERSC Case Study,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.351995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aa79afe5-5523-466d-b512-27b85aba7444 · outbound

This paper cites Anomaly detection: A survey,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Anomaly detection: A survey,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.340551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c8ed109-ffb8-40d1-bac6-3ed13b2f6d59 · outbound

This paper cites Outside the closed world: On using machine learning for network intrusion detection,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Outside the closed world: On using machine learning for network intrusion detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.275859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9a0a83ca-d181-4349-a442-c7a9a93f31e5 · outbound

This paper cites Challenging the anomaly detection paradigm: A provocative discussion,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Challenging the anomaly detection paradigm: A provocative discussion,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.136848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a8906291-9dbe-4a2c-b57f-d43dc0f5fec9 · outbound

This paper cites Learning nonstationary models of normal network traffic for detecting novel attacks,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Learning nonstationary models of normal network traffic for detecting novel attacks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.067121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7f7799ba-2588-44ac-9459-5ab8c12a6a4b · outbound

This paper cites A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.056846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8be0d45d-2e62-4d77-a824-9c0fc780e31e · outbound

This paper cites Isolation Forest,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Isolation Forest,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:27.047046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1fa90085-63d9-4c30-8495-f76dc0161250 · outbound

This paper cites LOF: identifying density-based local outliers,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining LOF: identifying density-based local outliers,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:26.969033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3e97fb50-fa4a-4cfd-9411-372a5c64dd8c · outbound

This paper cites Diagnosing network-wide traffic anomalies,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Diagnosing network-wide traffic anomalies,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:26.911954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 838c7e97-2255-4546-a4d9-5cb401dfce5f · outbound

This paper cites Long-term forecast- ing of Internet backbone traffic,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Long-term forecast- ing of Internet backbone traffic,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:26.901852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 034adecd-79bd-4216-9f1a-7c50c6ff1845 · outbound

This paper cites Predicting W AN traffic volumes using Fourier and multivariate SARIMA ap- proach,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Predicting W AN traffic volumes using Fourier and multivariate SARIMA ap- proach,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:26.891620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0353fd72-b148-4fa0-b090-93efaedafc5a · outbound

This paper cites A Comprehensive Study of Wide Area Data Movement at a Scientific Computing Facility.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining A Comprehensive Study of Wide Area Data Movement at a Scientific Computing Facility

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:26.881026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c11ceada-0503-4f57-bf6c-5a0bbc009d1c · outbound

This paper cites Traffic Prediction for Research and Education Networks using an Ensemble GRU-LSTM with Varying Lead Times,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Traffic Prediction for Research and Education Networks using an Ensemble GRU-LSTM with Varying Lead Times,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:26.871413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 277bd613-e66d-4ed4-ab1e-55ba4bd08023 · outbound

This paper cites Network Traffic Prediction based on Diffusion Con- volutional Recurrent Neural Networks,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Network Traffic Prediction based on Diffusion Con- volutional Recurrent Neural Networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:26.859813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dfbbae7a-e194-488b-a37a-2453d53082fc · outbound

This paper cites APRIL: An Application-Aware, Predictive and Intelligent Load Balancing So- lution for Data-Intensive Science,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining APRIL: An Application-Aware, Predictive and Intelligent Load Balancing So- lution for Data-Intensive Science,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:26.849860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation eac228e1-58ec-4a26-88d2-18e9179da5f0 · outbound

This paper cites Long-term Forecasting with TiDE: Time-series Dense Encoder.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Long-term Forecasting with TiDE: Time-series Dense Encoder

Reference 23

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Source-reported events for the cited work

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Observation fe9c88d7-396a-4e0d-8f1f-c3b3b6636f9c · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining A Time Series is Worth 64 Words: Long-term Forecasting with Transformers,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:26.839033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c42cff69-f2be-4c47-b6f6-c1ad0874537b · outbound

This paper cites Comparative Study of Big Data Visualization Tools and Techniques,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining Comparative Study of Big Data Visualization Tools and Techniques,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:26.828929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a5a3f551-38a0-4e54-a967-5ed3e7cfe37f · outbound

This paper cites A novel hybrid gldnn architecture for bangla dialect identification,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining A novel hybrid gldnn architecture for bangla dialect identification,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:26.756271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c3f72091-97e6-4e01-8d57-e6c79446ca65 · outbound

This paper cites A novel deep learning approach to predict air quality index,.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining A novel deep learning approach to predict air quality index,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:43:26.743776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4e864514-e05b-4daa-9d86-2cf64d15705d · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T05:43:26.679378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.