Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.00392.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T10:37:25.819654Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-16T12:16:17.039197Z
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 a3479f44-41cf-42f6-b3d3-e028db9a0790 · inbound
Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations cites this paper.
Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing
Reference 22
- Resolution
- verified exact
- local_arxiv, observed 2026-08-16T10:37:26.073110Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 63e869bd-ed37-435d-9d72-89a9fd9b42aa · inbound
EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability cites this paper.
EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T21:53:20.909079Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.