Paper Citation Record · LEDGER

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection

As of 23 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2507.21640.

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

pith.paper-citation-record.v1
2507.21640 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:35:49.643788Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

13 of 13 outbound references displayed

verified exact2
verified fuzzy10
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9b2596c2-994d-4883-a732-ad6b9c388854 · outbound

This paper cites Detecting can bus intrusion by applying machine learning method to graph based features.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection Detecting can bus intrusion by applying machine learning method to graph based features

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:35:50.251615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fccd99f4-3fd5-4c5c-a258-2e7ab308226f · outbound

This paper cites Survey of automotive controller area network intrusion detection systems.IEEE Design & Test, 36(6):48–55, 2019.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection Survey of automotive controller area network intrusion detection systems.IEEE Design & Test, 36(6):48–55, 2019

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:35:50.233361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5f28635f-f884-477d-b1c8-91a8d9eed842 · outbound

This paper cites Hackers remotely kill a jeep on the highway—with me in it.https:// www.wired.com/2015/07/hackers-remotely-kill-jeep-highway/.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection Hackers remotely kill a jeep on the highway—with me in it.https:// www.wired.com/2015/07/hackers-remotely-kill-jeep-highway/

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:35:50.212526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ef71cdfc-7d49-4a52-b850-57077f417048 · outbound

This paper cites Free-fall: Hacking tesla from wireless to can bus.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection Free-fall: Hacking tesla from wireless to can bus

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:35:50.186104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2ad5e5c3-304c-4a35-b35e-1a35e207f833 · outbound

This paper cites Car hacking and defense competition on in-vehicle network.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection Car hacking and defense competition on in-vehicle network

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:35:50.161196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fb5f4d6c-1249-4326-b4c0-852d6130a93f · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection Semi-Supervised Classification with Graph Convolutional Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T12:35:49.208479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3965e297-70e8-40e6-9b24-0f85f519ec9f · outbound

This paper cites Intrusion detection system based on the analysis of time intervals of can messages for in-vehicle network.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection Intrusion detection system based on the analysis of time intervals of can messages for in-vehicle network

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:35:50.135478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation af273877-683a-49c0-85b3-52080a2d242b · outbound

This paper cites Otids: A novel intrusion detectionsystemforin-vehiclenetworkbyusingremoteframe.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection Otids: A novel intrusion detectionsystemforin-vehiclenetworkbyusingremoteframe

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:35:50.114026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6184d36f-d0ad-4370-88ab-886d5d933118 · outbound

This paper cites In-vehicle network intrusion detection using deep convolutional neural network.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection In-vehicle network intrusion detection using deep convolutional neural network

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:35:50.091371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d8bcb1b7-4738-4219-9537-808449c56d7c · outbound

This paper cites Gdt-ids: Graph-based decision tree intrusion detection system for controller area network.The Journal of Supercomputing, 81(4):591, 2025.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection Gdt-ids: Graph-based decision tree intrusion detection system for controller area network.The Journal of Supercomputing, 81(4):591, 2025

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:35:50.066378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e71d8000-22ac-49a1-81b3-fb259694bdd9 · outbound

This paper cites Dgids: Dynamic graph-based intrusion detection system for can.Computers & Security, 147:104076, 2024.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection Dgids: Dynamic graph-based intrusion detection system for can.Computers & Security, 147:104076, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:35:50.044469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 43a39c32-6ad1-4bc7-8299-7c68003eea0a · outbound

This paper cites GCNIDS: Graph Convolutional Network-Based Intrusion Detection System for CAN Bus.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection GCNIDS: Graph Convolutional Network-Based Intrusion Detection System for CAN Bus

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:35:49.887981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d515839d-908c-46c7-b0bc-40dca7a47daa · outbound

This paper cites Car hacking: Attack & defense challenge 2020 dataset.https: //dx.doi.org/10.21227/qvr7-n418.

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection Car hacking: Attack & defense challenge 2020 dataset.https: //dx.doi.org/10.21227/qvr7-n418

Reference 13

Resolution: verified exact; doi, observed 2026-08-06T12:35:49.795245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.