Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2408.07583.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T17:11:46.972690Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T17:11:47.289097Z
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 e007769c-ac00-4691-b848-1bf551db42f6 · inbound
Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models cites this paper.
Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T17:11:47.294828Z
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.