Paper Citation Record · LEDGER

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus

As of 20 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2501.18821.

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

pith.paper-citation-record.v1
2501.18821 v3

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:24:13.437630Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

35 of 35 outbound references displayed

verified exact23
verified fuzzy4
unresolved6
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aa60fb5d-1c1b-405f-91cb-6c085749d274 · outbound

This paper cites Federated learning-based misbehavior detection for the 5G-enabled internet of vehicles,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Federated learning-based misbehavior detection for the 5G-enabled internet of vehicles,

Reference 1

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:16.842843Z

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 43a3098e-f813-426b-949f-ba6b7ca43948 · outbound

This paper cites CANival: A multimodal approach to intrusion detection on the vehicle CAN bus,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus CANival: A multimodal approach to intrusion detection on the vehicle CAN bus,

Reference 2

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:16.659858Z

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 cd94302c-1107-445b-8b02-efcfb95241a3 · outbound

This paper cites NovelADS: A novel anomaly detection system for intra-vehicular networks,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus NovelADS: A novel anomaly detection system for intra-vehicular networks,

Reference 3

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:16.490455Z

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 4c723819-e5e4-4fa1-a926-ccebe119a7a2 · outbound

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

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Car hacking and defense competition on in-vehicle network,

Reference 4

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:16.320396Z

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 2eb48dd7-2199-47d9-8427-b0039a172458 · outbound

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

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus In-vehicle network intrusion detection using deep convolutional neural network,

Reference 5

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:16.138901Z

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 dfcf0af7-7d12-4bf9-9b67-49002327d136 · outbound

This paper cites Classification of normal and malicious traffic based on an ensemble of machine learning for a vehicle CAN-network,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Classification of normal and malicious traffic based on an ensemble of machine learning for a vehicle CAN-network,

Reference 6

Resolution: verified exact; doi, observed 2026-08-09T22:24:13.516667Z

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 4809aa88-2e7c-4cf1-ad4d-586c6401925e · outbound

This paper cites V ANET network traffic anomaly detection using GRU-based deep learning model,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus V ANET network traffic anomaly detection using GRU-based deep learning model,

Reference 7

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:15.965059Z

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 ffa0ac7f-8ca5-4ddc-8a05-4a5485238e8e · outbound

This paper cites Machine learning based intrusion detection systems for connected autonomous vehicles: A survey,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Machine learning based intrusion detection systems for connected autonomous vehicles: A survey,

Reference 8

Resolution: verified exact; doi, observed 2026-08-09T22:24:13.508183Z

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 f0a50c28-714b-48bf-8d67-283bdd2b7edd · outbound

This paper cites Entropy- based genetic feature engineering and multi-classifier fusion for anomaly detection in vehicle controller area networks,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Entropy- based genetic feature engineering and multi-classifier fusion for anomaly detection in vehicle controller area networks,

Reference 9

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:15.766053Z

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 289b7005-3b8e-45c7-8cdc-8d0d06eb531c · outbound

This paper cites Novel deep learning-enabled LSTM autoencoder architecture for discovering anomalous events from intelligent transportation systems,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Novel deep learning-enabled LSTM autoencoder architecture for discovering anomalous events from intelligent transportation systems,

Reference 10

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:15.621361Z

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 135f1c1e-2ca3-4cd1-81f3-0f61255c31ad · outbound

This paper cites Self-supervised anomaly detection for in- vehicle network using noised pseudo normal data,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Self-supervised anomaly detection for in- vehicle network using noised pseudo normal data,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:24:16.875319Z

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 5e56badb-8d77-4744-8858-51154bbba6fa · outbound

This paper cites A federated learning framework for cyberattack detection in vehicular sensor networks,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus A federated learning framework for cyberattack detection in vehicular sensor networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:24:16.867674Z

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 0acce5f2-6ad2-4161-a80a-c0579fc46989 · outbound

This paper cites MGA-IDS: Optimal feature subset selection for anomaly detection framework on in-vehicle networks- CAN bus based on genetic algorithm and intrusion detection approach,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus MGA-IDS: Optimal feature subset selection for anomaly detection framework on in-vehicle networks- CAN bus based on genetic algorithm and intrusion detection approach,

Reference 13

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:15.295762Z

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 741f6e3b-df56-4656-961e-b8cfbde11e71 · outbound

This paper cites A novel intrusion detection model for the can bus packet of in-vehicle network based on attention mechanism and autoencoder,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus A novel intrusion detection model for the can bus packet of in-vehicle network based on attention mechanism and autoencoder,

Reference 14

Resolution: verified exact; doi, observed 2026-08-09T22:24:13.491529Z

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 452f1a16-a9f9-4930-833e-47e9b49d1ace · outbound

This paper cites OTIDS: A novel intrusion detection system for in-vehicle network by using remote frame,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus OTIDS: A novel intrusion detection system for in-vehicle network by using remote frame,

Reference 15

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:15.153591Z

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 e2df25f5-ec5c-431d-b27b-1215c1517dd8 · outbound

This paper cites Deep learning-based anomaly detection for connected autonomous vehicles using spatiotemporal information,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Deep learning-based anomaly detection for connected autonomous vehicles using spatiotemporal information,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:24:16.858119Z

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 6a6d4146-3b2b-445f-b840-56aa631b150c · outbound

This paper cites A lightweight intrusion detection model for in-vehicular CAN networks,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus A lightweight intrusion detection model for in-vehicular CAN networks,

Reference 17

Resolution: verified exact; doi, observed 2026-08-09T22:24:13.482900Z

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 a7e4b20f-17e4-4e2e-988f-66bebfdb0b05 · outbound

This paper cites Multi-order feature interaction-aware intrusion detection scheme for ensuring cyber security of intelligent connected vehicles,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Multi-order feature interaction-aware intrusion detection scheme for ensuring cyber security of intelligent connected vehicles,

Reference 18

Resolution: malformed identifier; arxiv_id_nonexistent, observed 2026-08-09T22:24:14.841272Z

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 8a685f8c-44f7-4cd4-973d-45b78c54955d · outbound

This paper cites Sustainable and lightweight domain-based intrusion detection system for in-vehicle network,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Sustainable and lightweight domain-based intrusion detection system for in-vehicle network,

Reference 19

Resolution: malformed identifier; arxiv_id_nonexistent, observed 2026-08-09T22:24:14.686762Z

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 063860c3-5c29-4950-93e1-0141561378e0 · outbound

This paper cites SMOTE: Synthetic minority over-sampling technique,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus SMOTE: Synthetic minority over-sampling technique,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T22:24:13.403840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c390dae9-a79d-4242-99ee-3e0a09c7d835 · outbound

This paper cites Deep Residual Learning for Image Recognition.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Deep Residual Learning for Image Recognition

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T22:24:13.406664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2436cb06-6d7c-46ff-919a-5b92509fb592 · outbound

This paper cites Forecasting vegetation behavior based on PlanetScope time series data using RNN-based models,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Forecasting vegetation behavior based on PlanetScope time series data using RNN-based models,

Reference 22

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:14.523942Z

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 13a8015e-f963-4daf-a52f-d5b8fb2400eb · outbound

This paper cites Long short-term memory,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Long short-term memory,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T22:24:13.412520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5672ab52-20a5-4547-aac3-1d5b9e4ff4da · outbound

This paper cites Gate-variants of gated recurrent unit (GRU) neural networks,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Gate-variants of gated recurrent unit (GRU) neural networks,

Reference 24

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:14.344779Z

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 1d1c578c-1d5b-4731-9fe2-0bb52b6d3294 · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus xLSTM: Extended Long Short-Term Memory

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T22:24:13.418019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a22da98-ff1e-4c7d-bca1-8a8d4837bd02 · outbound

This paper cites A novel model for student’s mental health monitoring based on hard and soft data fusion,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus A novel model for student’s mental health monitoring based on hard and soft data fusion,

Reference 26

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:14.160611Z

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 b94aca37-589f-4702-9b53-ce2b949c044c · outbound

This paper cites 1D-CNN-IDS: 1D CNN-based intrusion detection system for IIoT,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus 1D-CNN-IDS: 1D CNN-based intrusion detection system for IIoT,

Reference 27

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:14.001062Z

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 ab208047-4c42-4e49-a751-55ee626d1a45 · outbound

This paper cites Liggins II, D.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Liggins II, D

Reference 28

Resolution: verified exact; doi, observed 2026-08-09T22:24:13.465292Z

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 ed83aadc-ed91-4e15-9c42-2df668eb72b6 · outbound

This paper cites Cascaded feature fusion with multi-level self-attention mechanism for object detection,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Cascaded feature fusion with multi-level self-attention mechanism for object detection,

Reference 29

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:13.705022Z

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 4c98b92b-3f96-4770-885b-b1169d564abe · outbound

This paper cites HCRL website,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus HCRL website,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:24:16.850588Z

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 f33a8bab-bd26-4a5e-a8a4-e27359bb2547 · outbound

This paper cites Approximate statistical tests for comparing supervised classification learning algorithms,.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Approximate statistical tests for comparing supervised classification learning algorithms,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T22:24:13.434745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b81dc706-4de6-49a7-a5e8-5ade71758127 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus KAN: Kolmogorov-Arnold Networks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T22:24:13.437630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4c528a1-b757-4146-981d-5610100958d8 · outbound

This paper cites Available: https://doi.org/10.1109/TVT.2021.3051026.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Available: https://doi.org/10.1109/TVT.2021.3051026

Reference 2021

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:15.470626Z

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 f64d5924-933d-4c1a-a827-1263f1155a8d · outbound

This paper cites Available: https://doi.org/10.1007/s40747-022-00705-w.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Available: https://doi.org/10.1007/s40747-022-00705-w

Reference 2022

Resolution: verified exact; doi, observed 2026-08-09T22:24:13.499745Z

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 c1afde49-dfc9-4a99-8280-be976142acfe · outbound

This paper cites Available: https://doi.org/10.1109/TITS.2023.3286611.

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus Available: https://doi.org/10.1109/TITS.2023.3286611

Reference 2023

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T22:24:15.003384Z

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.

Pith citing papers

No inbound Pith citation observations are available.