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Paper Citation Record · LEDGER

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection

As of 8 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2608.01454.

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

pith.paper-citation-record.v1
2608.01454 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:12:45.339878Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

60 of 60 outbound references displayed

  • verified exact0
  • verified fuzzy43
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1b2534e7-e672-48d6-89e0-39727d96d701 · outbound

This paper cites Provenance-based in- trusion detection systems: A survey,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Provenance-based in- trusion detection systems: A survey,

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 98c97d73-1d67-43dd-b241-c19105a18bb5 · outbound

This paper cites Are we there yet? an industrial viewpoint on provenance-based endpoint detection and response tools,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Are we there yet? an industrial viewpoint on provenance-based endpoint detection and response tools,

Reference 2

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raw_fallback, observed 2026-08-06T00:12:52.490430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:41.252207Z digest=sha256:a6932c82e8ea605019dde6a03e4af5af629b308cebf1d40a1d425f18ea4d68fa

Observation 014bc13f-f0b5-4875-9526-f2dd8b852303 · outbound

This paper cites SLEUTH: Real-time attack scenario reconstruction from COTS audit data,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection SLEUTH: Real-time attack scenario reconstruction from COTS audit data,

Reference 3

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raw_fallback, observed 2026-08-06T00:12:52.345366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:41.315830Z digest=sha256:ea3592f4e23fced9595075d7c81bb14fcefda5d9a9e92876ce7ad24fd88a1d8c

Observation 081d34ce-49d4-4d64-8413-551b679cffc5 · outbound

This paper cites HOLMES: Real-time APT detection through correlation of suspicious information flows,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection HOLMES: Real-time APT detection through correlation of suspicious information flows,

Reference 4

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raw_fallback, observed 2026-08-06T00:12:52.152844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:41.358735Z digest=sha256:1ffce9369aafa74faadc82172f58c158c184fe865efc0261b5832c6316799d49

Observation b311dc26-245c-4469-84bb-8fb834565b0e · outbound

This paper cites Graph neural networks for intrusion detection: A survey,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Graph neural networks for intrusion detection: A survey,

Reference 5

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raw_fallback, observed 2026-08-06T00:12:52.012334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:41.460075Z digest=sha256:27e3c98fb7c0d87f5ec9250f871b2c30f9c796f3fd014bac5cadaae845d8b9be

Observation 28c913c6-7489-46f3-9d79-8b430dbdfe45 · outbound

This paper cites KAIROS: Practical intrusion detection and investigation using whole- system provenance,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection KAIROS: Practical intrusion detection and investigation using whole- system provenance,

Reference 6

Resolution
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raw_fallback, observed 2026-08-06T00:12:51.852889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:41.529829Z digest=sha256:59907d1b12fa523d148db1c132c26fa1ec333994879cbe2bc7054dd32a5df4eb

Observation 57564db8-8b78-48af-9613-782fb9a0d7de · outbound

This paper cites THREATRACE: Detecting and tracing host-based threats in node level through provenance graph learning,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection THREATRACE: Detecting and tracing host-based threats in node level through provenance graph learning,

Reference 7

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raw_fallback, observed 2026-08-06T00:12:51.729272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:41.589431Z digest=sha256:57be79309159a49670276056bb5525248713c214c3443d29e05db99a81a07720

Observation bfc4c7c5-d544-4ff6-886c-3f5ffdf1643f · outbound

This paper cites On the reproducibility of provenance-based intrusion detection that uses deep learning,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection On the reproducibility of provenance-based intrusion detection that uses deep learning,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:41.622204Z digest=sha256:3acfec3c76f4a533def6913d02244de05349633e8142d388e9a312a3922c0cd6

Observation d583f4ce-ffed-4405-87ed-a29a8c0955f7 · outbound

This paper cites Sometimes simpler is better: A comprehensive analysis of state-of-the-art provenance-based intrusion detection systems,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Sometimes simpler is better: A comprehensive analysis of state-of-the-art provenance-based intrusion detection systems,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:41.683413Z digest=sha256:3d2274b7b9651fd69de895da82878856553f7339ca8e272508495d5d0739255c

Observation ffa7a138-40ae-4934-b34d-aae264bdb4fe · outbound

This paper cites What we talk about when we talk about logs: Understanding the effects of dataset quality on endpoint threat detection research,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection What we talk about when we talk about logs: Understanding the effects of dataset quality on endpoint threat detection research,

Reference 10

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raw_fallback, observed 2026-08-06T00:12:51.395012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:41.768339Z digest=sha256:2354463e730ab4ebbae7ae6ca7246fb592b01d34c4f9e2024136172970661c25

Observation 68a231a9-cf9c-4a5c-840e-8881cf72fbdb · outbound

This paper cites On the forensic validity of approximated audit logs,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection On the forensic validity of approximated audit logs,

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T00:12:41.816860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:41.816860Z digest=sha256:04edeac3fc2f4847dd9d2c8040f96d903b5e230c98a7f68313beff5aa9a5382d

Observation 31df0c72-4f68-4d0e-b267-369010e1d746 · outbound

This paper cites Shortcut learning in deep neural networks,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Shortcut learning in deep neural networks,

Reference 12

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unresolved
no resolver link, observed 2026-08-06T00:12:41.908831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:41.908831Z digest=sha256:66b1f1c50caf5d097a65c81697312f13ee184d5b5af508d27b7f4d9766ba410a

Observation 398fe4c0-2b44-4991-aafe-276d73b4720e · outbound

This paper cites ORTHRUS: Achieving high quality of attribution in provenance-based intrusion detection systems,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection ORTHRUS: Achieving high quality of attribution in provenance-based intrusion detection systems,

Reference 13

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raw_fallback, observed 2026-08-06T00:12:51.201616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:41.962118Z digest=sha256:707d359358058e7e78d102dc63554eb2cda6ec4187f698cfcd19fee9ac37c230

Observation a66f594c-388c-4de3-9a74-e21cd1251432 · outbound

This paper cites NoDoze: Combatting threat alert fatigue with automated provenance triage,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection NoDoze: Combatting threat alert fatigue with automated provenance triage,

Reference 14

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raw_fallback, observed 2026-08-06T00:12:51.060852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:42.018467Z digest=sha256:3930c378a8cb6ab38efe5d6eb1586e4f27dd10cc8d7915e7890e1118e7240ce7

Observation 19e194f4-3f25-4c0e-88c3-7a1c44a28dbe · outbound

This paper cites Back-Propagating system dependency impact for attack investigation,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Back-Propagating system dependency impact for attack investigation,

Reference 15

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raw_fallback, observed 2026-08-06T00:12:50.921794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:42.150437Z digest=sha256:d2f8e5f1dcc6607de49fb410fb6e92f6d22673a8fd36598ea85d3f7cffe709cd

Observation 309684a1-d780-40a6-ac80-03f61b054ede · outbound

This paper cites NODLINK: An online system for fine-grained apt attack detection and investigation,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection NODLINK: An online system for fine-grained apt attack detection and investigation,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:42.190903Z digest=sha256:bf2b9f361bd293304ab1540cb2504b3a624b7a8af5605b2b6ddd237584e4c0ae

Observation fa5112cd-33b8-4f85-88ea-0ce5ab7a2755 · outbound

This paper cites Dos and don’ts of machine learning in computer security,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Dos and don’ts of machine learning in computer security,

Reference 17

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no resolver link, observed 2026-08-06T00:12:42.219769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:42.219769Z digest=sha256:61e0472825b6f72f39e06f4b96674387e00e5ace31111d7cc6d93c356f43264c

Observation b6e70a15-8dca-4536-bc71-b82b5e68ea24 · outbound

This paper cites Transparent computing engagement 3 data release,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Transparent computing engagement 3 data release,

Reference 18

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raw_fallback, observed 2026-08-06T00:12:50.587011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:42.261558Z digest=sha256:7663eb74ad11d78ffa151bf388250d4cd825ceeb8f2fa3761b1a4bfcba60c358

Observation 384d63fe-4220-43c8-b976-5998713fafec · outbound

This paper cites REAPr: Recovery every attack process,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection REAPr: Recovery every attack process,

Reference 19

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raw_fallback, observed 2026-08-06T00:12:50.474000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:42.344739Z digest=sha256:03e244214914b6eeb6b81b9eaa69a32e86aaae715b0474397aa86253fec3b7fc

Observation 8082b64b-40cc-4ee0-a338-61ed37012353 · outbound

This paper cites ATLASv2: ATLAS Attack Engagements, Version 2.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection ATLASv2: ATLAS Attack Engagements, Version 2

Reference 20

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no resolver link, observed 2026-08-06T00:12:42.400804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:42.400804Z digest=sha256:18d3f31e8de9c1e43439e5bd715ffe82bea2e096bb8f602422a2e35a60620f10

Observation ba49a220-ad07-4dbd-89e8-7423264bf2a5 · outbound

This paper cites How to effectively trace provenance on windows endpoint detection & response telemetry,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection How to effectively trace provenance on windows endpoint detection & response telemetry,

Reference 21

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raw_fallback, observed 2026-08-06T00:12:50.244315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:42.458312Z digest=sha256:2796b8bf7939c705c464bfe204f54fdc056af999c46b63171023e3688849291d

Observation 4d9d56f6-bbcf-4e75-a50d-342b5b4e04e7 · outbound

This paper cites SPADE: Support for provenance auditing in distributed environments,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection SPADE: Support for provenance auditing in distributed environments,

Reference 22

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raw_fallback, observed 2026-08-06T00:12:50.075333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:42.557089Z digest=sha256:24c9f214806a5fe942266595fe002920fdfe05fd80efc4908497b8537226882c

Observation 078e3ad3-9a76-42cc-ba7f-27878d15cde8 · outbound

This paper cites Practical whole-system provenance capture,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Practical whole-system provenance capture,

Reference 23

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raw_fallback, observed 2026-08-06T00:12:49.887090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:42.606910Z digest=sha256:e00303c2c4584e4e4b10d5eb14469dabcab093d54a26f684bd7aaa55d59e7a84

Observation 1c389a06-d7c4-4039-9d82-197e15daa6d7 · outbound

This paper cites Trustworthy Whole- System provenance for the linux kernel,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Trustworthy Whole- System provenance for the linux kernel,

Reference 24

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unresolved
no resolver link, observed 2026-08-06T00:12:42.724756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:42.724756Z digest=sha256:2deeaded37eaf59e9d91607dc1ce2752387c7298a1ed2a59b2b7ca8df927fa15

Observation 6e5af8c3-4017-4041-8be1-eafd7dc0a266 · outbound

This paper cites Loggc: garbage collecting audit log,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Loggc: garbage collecting audit log,

Reference 25

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raw_fallback, observed 2026-08-06T00:12:49.714635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:42.830699Z digest=sha256:013d35b48ff3b17cabac863d2278d3b34a087e3ea897ace51c7511dea13a3a90

Observation 5a2182e3-c245-4834-8eef-f96bde56f6a8 · outbound

This paper cites High accuracy attack provenance via binary-based execution partition,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection High accuracy attack provenance via binary-based execution partition,

Reference 26

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raw_fallback, observed 2026-08-06T00:12:49.523997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:42.895706Z digest=sha256:0d9783f30ea30f33dd96994d0923f642b4b3d201ec854e1421dac313cffc6ead

Observation 49df516c-23f0-4e9d-b3c9-c95197c068d2 · outbound

This paper cites High fidelity data reduction for big data security dependency analyses,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection High fidelity data reduction for big data security dependency analyses,

Reference 27

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no resolver link, observed 2026-08-06T00:12:42.961980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:42.961980Z digest=sha256:2a7c69b242ba7721c47f5172d12c314bfb46f89b0753b44e5f051152d283aaba

Observation fa2f49b0-76b1-45e6-804e-a6b1c0886d99 · outbound

This paper cites Towards a timely causality analysis for enterprise security,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Towards a timely causality analysis for enterprise security,

Reference 28

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raw_fallback, observed 2026-08-06T00:12:49.364034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:43.011954Z digest=sha256:2f89cb2b076a86c82dab7df33c74d6018aa12ea330abc0682ff92387939ad481

Observation 8864fcf1-f417-4aea-ba39-fce16ae2497d · outbound

This paper cites W ATSON: Abstracting behaviors from audit logs via aggregation of contextual semantics,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection W ATSON: Abstracting behaviors from audit logs via aggregation of contextual semantics,

Reference 29

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raw_fallback, observed 2026-08-06T00:12:49.247072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:43.109348Z digest=sha256:f14e003e54e1c18a181935382c2dc9cdf20561148a7e3344dc7abf8e04e59d27

Observation 2c8da824-15bc-41a8-8fdd-d3e75db6057b · outbound

This paper cites DeepLog: Anomaly detection and diagnosis from system logs through deep learning,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection DeepLog: Anomaly detection and diagnosis from system logs through deep learning,

Reference 30

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raw_fallback, observed 2026-08-06T00:12:49.138030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:43.162226Z digest=sha256:397abf73a227b21348188161f8f147c80b8e3b361f2b82924f5608c90680ba3b

Observation ca22fd31-f60c-4a52-8ee3-b7daa313a44d · outbound

This paper cites Logbert: Log anomaly detection via bert,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Logbert: Log anomaly detection via bert,

Reference 31

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unresolved
no resolver link, observed 2026-08-06T00:12:43.236308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:43.236308Z digest=sha256:55b661e03cb72d4ea5d427b4af07b3ac873a14794a9c45ac670a018e976a2d52

Observation 817068f6-d3c9-4b24-9c84-8fbf1f897b22 · outbound

This paper cites Unicorn: Runtime provenance-based detector for advanced persistent threats,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Unicorn: Runtime provenance-based detector for advanced persistent threats,

Reference 32

Resolution
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raw_fallback, observed 2026-08-06T00:12:49.022650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:43.288466Z digest=sha256:0cec2cb4b2050bd8153f7816ca3e9e432e0844f1b29a163454420fb2a6564d35

Observation cbb42c45-b1e5-4c75-969d-bb37a4bf647e · outbound

This paper cites You are what you do: Hunting stealthy malware via data provenance analysis,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection You are what you do: Hunting stealthy malware via data provenance analysis,

Reference 33

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raw_fallback, observed 2026-08-06T00:12:48.902383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:43.353304Z digest=sha256:b508366343e0c523d5a4c428f3a3d7de4efcd9895e453cdb499b86ef9311f496

Observation c347928f-6746-4ebb-8f42-dcb10f5e98e9 · outbound

This paper cites MAGIC: Detecting advanced persistent threats via masked graph representation learning,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection MAGIC: Detecting advanced persistent threats via masked graph representation learning,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T00:12:48.736900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:43.450297Z digest=sha256:1d53a57a1cf320dae2ef9c78422a5225277ac5ed65ae327e806ed1ce29cb4e84

Observation d9de7929-3d37-499f-ad09-65429f726d7e · outbound

This paper cites Flash: A comprehensive approach to intrusion detection via provenance graph representation learning,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Flash: A comprehensive approach to intrusion detection via provenance graph representation learning,

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-06T00:12:48.530584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:43.548591Z digest=sha256:60e15913bf6a42eae2255dbc9ba331bc6c50d0ebb11d3574ffc8c1264e8c3cb7

Observation 518464f2-9bf4-4cc5-950e-8ad3fe6978be · outbound

This paper cites R-caid: Embedding root cause analysis within provenance-based intrusion detection,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection R-caid: Embedding root cause analysis within provenance-based intrusion detection,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T00:12:48.328184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:43.623182Z digest=sha256:cabe763c89aaa8ea7d3b25505ba4b3c55925bb3e10cf165c6c6a34058cb9b45f

Observation a660ef32-ceeb-4133-962d-4f96970811de · outbound

This paper cites The relationship between precision-recall and roc curves,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection The relationship between precision-recall and roc curves,

Reference 37

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no resolver link, observed 2026-08-06T00:12:43.689445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:43.689445Z digest=sha256:39849f3e6e8121830a70993ea03c2612041b244da7e7bce705fea2c11dde9b19

Observation 878e8b01-7f42-4ddd-9746-a07b099e58fa · outbound

This paper cites The precision-recall plot is more informa- tive than the ROC plot when evaluating binary classifiers on imbalanced datasets,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection The precision-recall plot is more informa- tive than the ROC plot when evaluating binary classifiers on imbalanced datasets,

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-06T00:12:48.133903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:43.756642Z digest=sha256:e34a711e9faabde4236ab2f0bbc3cf93091c49b2475b71baf3c6650aca3c7df6

Observation eac5dabe-bbc9-43b9-a83b-f70be375ea83 · outbound

This paper cites The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-06T00:12:47.912820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:43.841500Z digest=sha256:502a1bc0cd6ce49b956f4dcbde009df0355f78dbfb34f2391de8ade6a91acd4c

Observation 9c6f43c3-6e4c-471b-8fb4-4836901aa0cf · outbound

This paper cites TESSERACT: Eliminating experimental bias in malware classification across space and time (extended version),.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection TESSERACT: Eliminating experimental bias in malware classification across space and time (extended version),

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T00:12:47.671281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:43.943587Z digest=sha256:f30fd534f2946bb9df0362e3a9a490e9fdc3cc4a24991fc7e29e6d46dc302d44

Observation 5fcbca1c-a2d7-4a27-8582-b33706da386b · outbound

This paper cites SoK: Pragmatic assessment of machine learning for network intrusion detection,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection SoK: Pragmatic assessment of machine learning for network intrusion detection,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T00:12:47.488415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:44.070227Z digest=sha256:013f648e9b926920d5c3dca6f54207e35babfcc274a10be13cdfd4d685bc1ee3

Observation ce8503cc-bd4d-4196-8fc8-cda7aa5c144a · outbound

This paper cites Reporting score distributions makes a difference: Performance study of lstm-networks for sequence tagging,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Reporting score distributions makes a difference: Performance study of lstm-networks for sequence tagging,

Reference 42

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verified fuzzy
raw_fallback, observed 2026-08-06T00:12:47.372132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:44.145158Z digest=sha256:63d57326c342b2ee27c5c874d73f5b2f7380d93425528b78d26e228428a752df

Observation d797dbc1-fe65-44e4-aa57-8ae18cb42e19 · outbound

This paper cites Accounting for variance in machine learning benchmarks,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Accounting for variance in machine learning benchmarks,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T00:12:47.271093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:44.217483Z digest=sha256:375787ee99abf81b4c54dec3a6fb66edfda4ac968806202315ddb288f35f795a

Observation 30d1f2f5-e5b3-407c-b2ac-3aa28af46cf6 · outbound

This paper cites ATLAS: A sequence-based learning approach for attack investigation,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection ATLAS: A sequence-based learning approach for attack investigation,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T00:12:47.122348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:44.322975Z digest=sha256:80ac58e55626823bf4f8f0ccfb8f2fd0023ee27de550d06d33938e1584a928d2

Observation a5724c73-9983-40bb-bfb8-77df8c70a2d6 · outbound

This paper cites A new hope for darpa optc,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection A new hope for darpa optc,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T00:12:47.023073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:44.400531Z digest=sha256:6bf05b1632e9c5a01cacf5bb12d4ece9dd728025e9ac92e35a1de40f0a080689

Observation cdae3c81-148e-4413-adc2-213d4c701500 · outbound

This paper cites A mathematical theory of communication,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection A mathematical theory of communication,

Reference 46

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unresolved
no resolver link, observed 2026-08-06T00:12:44.459624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:44.459624Z digest=sha256:c2c27aae66c66c9e2eba1d9eeb007ca968f9b4ba4bf248fbb6b6210a57554899

Observation d41d2e39-d3ca-40b5-b797-9f851fe0009a · outbound

This paper cites Survivalism: Systematic analysis of Windows malware living- off-the-land,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Survivalism: Systematic analysis of Windows malware living- off-the-land,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T00:12:46.913789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:44.518711Z digest=sha256:156bb1912288c20e09f44d3b3f074f0bbd185d4ddfab08855c13487770ad4d7c

Observation cc61a368-ea8c-416f-a613-4d0a5e386c39 · outbound

This paper cites Self-supervised learning of graph representations for network intrusion detection,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Self-supervised learning of graph representations for network intrusion detection,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T00:12:46.747486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:44.600615Z digest=sha256:73c9b6e146417e1a043574f43cce47e56ce5231aea4072477507a0c5ff882421

Observation 7fab8737-07e1-4e1b-a694-82727c9141be · outbound

This paper cites E- GraphSAGE: A graph neural network based intrusion detection system for IoT,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection E- GraphSAGE: A graph neural network based intrusion detection system for IoT,

Reference 49

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verified fuzzy
raw_fallback, observed 2026-08-06T00:12:46.594080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:44.687439Z digest=sha256:bd31d0b79a363adf0ec5a87fdb672a0db0cbf863daa7daeb8860e999c8a676bc

Observation 39fae256-bedb-426e-b059-148129e59ebb · outbound

This paper cites Attention is all you need,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Attention is all you need,

Reference 50

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unresolved
no resolver link, observed 2026-08-06T00:12:44.758185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:44.758185Z digest=sha256:dd1a7a4bf12b47cad4834b7ea2094a7b105aa7d486b2309c8611529d7c8042ae

Observation 465093ae-7a6d-4c2c-b48b-4b7642618196 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Efficient Estimation of Word Representations in Vector Space

Reference 51

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unresolved
no resolver link, observed 2026-08-06T00:12:44.815942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:44.815942Z digest=sha256:ce2aa444d642f2c0eb3303995e091e5f42bf69cf39ac43060dc4c4dc9e9781fd

Observation 517f5e5f-8ea7-44f5-b810-f0a53eca5e9e · outbound

This paper cites PIDSMaker: An ml framework for building provenance- based intrusion detection systems,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection PIDSMaker: An ml framework for building provenance- based intrusion detection systems,

Reference 52

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verified fuzzy
raw_fallback, observed 2026-08-06T00:12:46.454123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:44.878240Z digest=sha256:62678a9aa18d78d616ab3dce5c6b3169ac46c66f4ffa0e46bb09ea35c6b44914

Observation c6ff909b-ad66-4dd9-b3f1-1b4b23daa5d2 · outbound

This paper cites MAGIC: Official implementation,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection MAGIC: Official implementation,

Reference 53

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raw_fallback, observed 2026-08-06T00:12:46.340069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:44.938287Z digest=sha256:e7868bbd36f9df8b71069f8fd7700cb36625fcc911d313401988e3792a9a4dee

Observation c5d7783f-995e-4c65-94fc-d4a6b16c7930 · outbound

This paper cites PyTorch: An imperative style, high-performance deep learning library,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection PyTorch: An imperative style, high-performance deep learning library,

Reference 54

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unresolved
no resolver link, observed 2026-08-06T00:12:44.998573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:44.998573Z digest=sha256:545e93f553e7e84c840a9c7d69415f4be9b914d0104d776143853a075785dd6a

Observation 7acf3fce-6595-4174-b44a-f44838b9ed54 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Fast Graph Representation Learning with PyTorch Geometric

Reference 55

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unresolved
no resolver link, observed 2026-08-06T00:12:45.066398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:45.066398Z digest=sha256:e695e20298d445d50619f80bced9a546657c1d1a6997051ed26340f09bb8d2c0

Observation e0fe0935-6403-45dc-b53e-2e69506286a3 · outbound

This paper cites Decoupled weight decay regularization,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Decoupled weight decay regularization,

Reference 56

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unresolved
no resolver link, observed 2026-08-06T00:12:45.133827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:45.133827Z digest=sha256:f8aef5a22209e3d59d858351c3658a88056854b10e4a4a24c1dc7f63c9be0f40

Observation e814970b-1a85-4f59-ad7e-52324320c762 · outbound

This paper cites Datasheets for datasets,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Datasheets for datasets,

Reference 57

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raw_fallback, observed 2026-08-06T00:12:46.207367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:45.212720Z digest=sha256:02478a3d6878204ef18df47fd92e8d438f68bff98d3ef8ebaee17f390188ff7e

Observation 7712aa8d-b2a5-43fc-af26-6b98f82b9b88 · outbound

This paper cites The Menlo Report: Ethical principles guiding information and communication technology research,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection The Menlo Report: Ethical principles guiding information and communication technology research,

Reference 58

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verified fuzzy
raw_fallback, observed 2026-08-06T00:12:46.080576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:12:45.286712Z digest=sha256:2ffdc3e3d242eada66f651a42bfa0907ee1ae128572d09185f15147e412cfd1a

Observation c8fc40f3-198a-4837-82cd-d7d1048d2491 · outbound

This paper cites Fast memory-efficient anomaly detection in streaming heterogeneous graphs,.

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection Fast memory-efficient anomaly detection in streaming heterogeneous graphs,

Reference 59

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unresolved
no resolver link, observed 2026-08-06T00:12:45.339878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:45.339878Z digest=sha256:185eb0440a8b7aa1d2d8497d22c8450795475c1278d8e7ddb52686826452fa22

Observation 682bef6d-5863-4aa0-bdcd-db50a4b18a78 · outbound

This paper cites TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version).

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)

Reference 2025

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unresolved
no resolver link, observed 2026-08-06T00:12:44.013859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:12:44.013859Z digest=sha256:c9284b05932a2973da2ef11a03855d48ef4319cd360a5bf3b30ae47bfb7c42c6

Pith citing papers

No inbound Pith citation observations are available.