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Paper Citation Record · LEDGER

Explaining Network Intrusion Detection System Using Explainable AI Framework

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2103.07110.

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

pith.paper-citation-record.v1
2103.07110 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:44:17.610823Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-22T07:54:43.089215Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 273bac78-ffee-47bc-a7db-2bed182f6adf · inbound

Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems cites this paper.

Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems Explaining Network Intrusion Detection System Using Explainable AI Framework

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-12T20:44:17.610823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:44:17.610823Z digest=sha256:4199de60b3c8e081ca2f42423f63d572ea9069bc75b23d36b7679a6d61b674de

Observation 70a5d87a-6eab-4040-a1b7-9b77496464c6 · inbound

Evaluating explainable AI for deep learning-based network intrusion detection system alert classification cites this paper.

Evaluating explainable AI for deep learning-based network intrusion detection system alert classification Explaining Network Intrusion Detection System Using Explainable AI Framework

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T05:27:33.267727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:27:33.267727Z digest=sha256:b398ae53522ada0d14fbaa699da83ffc7411fb10d724af16be629455726aa31c

Observation 2b6cd157-9037-4d30-b7d5-01e8a827bb67 · inbound

Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets cites this paper.

Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets Explaining Network Intrusion Detection System Using Explainable AI Framework

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-22T07:54:43.091761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-22T07:54:15.327019Z digest=sha256:ef422bdd270a3293c0e757efc5c94eb09fb60ba249f2878255434d36f597e361