SoK: Federated Learning for Intrusion Detection in Vehicular Networks | Paper Citation Record

Observation d0bb7154-4837-4110-bd5f-da06c836f712 · outbound

This paper cites Ai-based intrusion detection systems for in-vehicle networks: A survey,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Ai-based intrusion detection systems for in-vehicle networks: A survey,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9dd50bd-df64-45c5-a54d-63466ddab7d7 · outbound

This paper cites Securing vehicle- to-everything (v2x) communication platforms,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Securing vehicle- to-everything (v2x) communication platforms,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 072fd3eb-698b-49b1-b291-10378458cb40 · outbound

This paper cites Selecting optimal features for cross-fleet analysis and fault diagnosis of industrial gas turbines,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Selecting optimal features for cross-fleet analysis and fault diagnosis of industrial gas turbines,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8cf7e00-d390-4dab-9415-ba9ff35e7960 · outbound

This paper cites Federated learning for intrusion detection systems in internet of vehicles: A general taxonomy, applications, and future directions,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Federated learning for intrusion detection systems in internet of vehicles: A general taxonomy, applications, and future directions,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f278a3e5-5f47-49c1-9176-6c87ef6d2d7d · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Communication-efficient learning of deep networks from decentralized data,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab17b200-a673-4f58-8510-e145ae0aace2 · outbound

This paper cites The prisma 2020 statement: an updated guideline for reporting systematic reviews,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks The prisma 2020 statement: an updated guideline for reporting systematic reviews,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b868927-c204-423c-bed9-0fd40201eb9a · outbound

This paper cites Federated learning for intrusion detection system: Concepts, challenges and future directions,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Federated learning for intrusion detection system: Concepts, challenges and future directions,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b2ca0b1-24d3-4b63-9e9d-00662e3d70d2 · outbound

This paper cites A review of federated learning applications in intrusion detection systems,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A review of federated learning applications in intrusion detection systems,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11678924-7d5c-45bd-8b1a-e8925713cea6 · outbound

This paper cites A survey of deep learning-based intrusion detection in automotive applications,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A survey of deep learning-based intrusion detection in automotive applications,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bae66c22-6707-425c-bfe9-82284516e6fd · outbound

This paper cites State-of-the-Art Survey on In-Vehicle Network Communication (CAN-Bus) Security and Vulnerabilities.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks State-of-the-Art Survey on In-Vehicle Network Communication (CAN-Bus) Security and Vulnerabilities

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ba194d1-ff9a-4ac3-a6a9-c854303f9d40 · outbound

This paper cites Cyberattacks and coun- termeasures for in-vehicle networks,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Cyberattacks and coun- termeasures for in-vehicle networks,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27e2d32a-7527-4174-a1f5-9b42a7cbe5e4 · outbound

This paper cites A survey of intrusion detection for in-vehicle networks,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A survey of intrusion detection for in-vehicle networks,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f8b931a-cec4-4dad-aedc-8ca6d6d26ec3 · outbound

This paper cites A Survey of Anomaly Detection in In-Vehicle Networks.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A Survey of Anomaly Detection in In-Vehicle Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4209f20d-2148-4db6-a38e-922374597d04 · outbound

This paper cites Simulation framework for misbehavior detection in vehicular net- works,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Simulation framework for misbehavior detection in vehicular net- works,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a1b7412-e8fd-462b-957e-7d3ae2d74a61 · outbound

This paper cites Vehicular edge computing and networking: A survey,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Vehicular edge computing and networking: A survey,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb42f40c-bcda-40ee-9b1c-cfa4abe8f159 · outbound

This paper cites Experimental security analysis of a modern automobile,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Experimental security analysis of a modern automobile,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20eb5308-4625-4b14-9629-b8af6baa8a22 · outbound

This paper cites A survey on can bus protocol: Attacks, challenges, and potential solutions,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A survey on can bus protocol: Attacks, challenges, and potential solutions,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92c3d78c-6bf1-4b05-8186-140dc2ffbb0f · outbound

This paper cites Remote exploitation of an unaltered passenger vehicle,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Remote exploitation of an unaltered passenger vehicle,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 957e17e7-2e1c-42b1-baf8-cf8cb6531cf9 · outbound

This paper cites A comprehensive guide to can ids data and introduction of the road dataset,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A comprehensive guide to can ids data and introduction of the road dataset,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7cd32c6-bce2-4a29-891b-c26c31bf4033 · outbound

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

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Otids: A novel intrusion detection system for in-vehicle network by using remote frame,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b97c30b-e43a-495e-a678-741bfb67dbdb · outbound

This paper cites Comprehensive experimental analyses of automotive attack surfaces,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Comprehensive experimental analyses of automotive attack surfaces,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca1024d1-7cf5-47f1-aa5a-d21b0d8293f9 · outbound

This paper cites Spoofing attack using bus-off attacks against a specific ecu of the can bus,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Spoofing attack using bus-off attacks against a specific ecu of the can bus,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9284d074-2559-4cdd-8583-83c18b4770d5 · outbound

This paper cites A comprehensive survey of v2x cybersecurity mechanisms and future research paths,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A comprehensive survey of v2x cybersecurity mechanisms and future research paths,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 642e96e6-0faa-4cd5-a999-bdd5f45929d4 · outbound

This paper cites Dedicated short-range communications (dsrc) standards in the united states,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Dedicated short-range communications (dsrc) standards in the united states,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3644639-45cc-4265-8eb4-7c10053c8c05 · outbound

This paper cites C-V2X Security Requirements and Procedures: Survey and Research Directions.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks C-V2X Security Requirements and Procedures: Survey and Research Directions

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fab04dc8-b387-4caa-bf94-c9503dda0b1f · outbound

This paper cites A survey and comparative analysis of methods for countering sybil attacks in vanets,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A survey and comparative analysis of methods for countering sybil attacks in vanets,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1577394e-7689-47c5-a098-736e482ec643 · outbound

This paper cites Detection and localization of sybil attack CYBER-AI 2026 10 in vanet: a review,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Detection and localization of sybil attack CYBER-AI 2026 10 in vanet: a review,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 741409e9-e1a8-4c32-88b9-10b37d4833c4 · outbound

This paper cites A sensor fusion-based gnss spoofing attack detection framework for autonomous vehicles,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A sensor fusion-based gnss spoofing attack detection framework for autonomous vehicles,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98a29ee5-4568-498d-bdbe-266ad2250d85 · outbound

This paper cites Cyber security challenges and solutions for v2x communications: A survey,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Cyber security challenges and solutions for v2x communications: A survey,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50bbf2f2-453a-4cc9-a981-35f82d9ef39a · outbound

This paper cites Threats and defenses in the federated learning life cycle: A comprehensive survey and challenges,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Threats and defenses in the federated learning life cycle: A comprehensive survey and challenges,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d74690d5-a03d-46fb-a029-b23163c2a9f9 · outbound

This paper cites Data poisoning attacks against federated learning systems,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Data poisoning attacks against federated learning systems,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d571dc0-e7c4-44ed-acfa-f5db5d29545f · outbound

This paper cites How to backdoor federated learning,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks How to backdoor federated learning,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e57dbded-ff33-4e0a-ac50-efec2250e2e6 · outbound

This paper cites Membership inference attacks against machine learning models via prediction sensitivity,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Membership inference attacks against machine learning models via prediction sensitivity,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3966f20-872a-48e8-934a-2706c160117d · outbound

This paper cites Federated learning based ids ap- proach for the iov,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Federated learning based ids ap- proach for the iov,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51abead2-a021-4a8c-a319-1d63a16985fd · outbound

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

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A federated learning framework for cyberattack detection in vehicular sensor networks,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ce58100-7a21-448b-944b-351566ad5cd4 · outbound

This paper cites A robust multi-stage intrusion detection system for in-vehicle network security using hierarchical federated learning,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A robust multi-stage intrusion detection system for in-vehicle network security using hierarchical federated learning,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79630e3f-bd71-435f-a4ad-dcf70be70a10 · outbound

This paper cites A federated learning–enabled secure and scalable sdn framework for energy-efficient vanets,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A federated learning–enabled secure and scalable sdn framework for energy-efficient vanets,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adee9258-7de8-4e51-8b55-04abe9feefb9 · outbound

This paper cites Mobility- aware cooperative caching in iovs based on secure asynchronous feder- ated and deep reinforcement learning,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Mobility- aware cooperative caching in iovs based on secure asynchronous feder- ated and deep reinforcement learning,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edd68de9-f19a-45b6-a7f2-923726eac6f2 · outbound

This paper cites Lstm-based intrusion detection system for in-vehicle can bus commu- nications,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Lstm-based intrusion detection system for in-vehicle can bus commu- nications,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cad4c61-04ba-43f9-beae-0c6981594dbf · outbound

This paper cites Misbehavior detection with spatio-temporal graph neural networks,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Misbehavior detection with spatio-temporal graph neural networks,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 931b7b90-0f31-467a-8601-0e6fb0850570 · outbound

This paper cites Spatiotemporal graph neural network-driven anomaly detection for cooperative vehicle messaging in dense vanet corridors,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Spatiotemporal graph neural network-driven anomaly detection for cooperative vehicle messaging in dense vanet corridors,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26b2e9fd-ad4b-450c-9588-bfcbe7e55a39 · outbound

This paper cites Federated optimization in heterogeneous networks,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Federated optimization in heterogeneous networks,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c71293a-55eb-49d8-a996-9132bda768e9 · outbound

This paper cites Ma- chine learning with adversaries: Byzantine tolerant gradient descent,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Ma- chine learning with adversaries: Byzantine tolerant gradient descent,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b68b15b2-1d32-420a-b7a4-98e83106f76c · outbound

This paper cites Local model poisoning attacks to{Byzantine-Robust}federated learning,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Local model poisoning attacks to{Byzantine-Robust}federated learning,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 934c3014-3487-4814-95c8-f26d6e45a879 · outbound

This paper cites FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bf44e19-e757-4ca0-85bd-95c77e064c3c · outbound

This paper cites Federated Learning with Personalization Layers.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Federated Learning with Personalization Layers

Reference 46

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea028ccc-e981-4ee5-bf5e-a637f855c871 · outbound

This paper cites Personalized federated learning with moreau envelopes,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Personalized federated learning with moreau envelopes,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2db1ce53-35f4-4d1e-87e4-d86d7252ac87 · outbound

This paper cites Threats to Federated Learning: A Survey.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Threats to Federated Learning: A Survey

Reference 48

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e0014b0-f1bf-456d-9b2b-95453936aa57 · outbound

This paper cites Deep learning with differential privacy,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Deep learning with differential privacy,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66136bbb-5453-4c07-be96-d304f88bbd52 · outbound

This paper cites Practical secure aggregation for privacy-preserving machine learning,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Practical secure aggregation for privacy-preserving machine learning,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc935835-1abe-4413-997d-d4ce86424046 · outbound

This paper cites Privacy-preserving deep learning via additively homomorphic encryption,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Privacy-preserving deep learning via additively homomorphic encryption,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9410ed80-2a88-46e4-82d4-ede9c1137456 · outbound

This paper cites Privacy-preserving byzantine-robust federated learning via deep reinforcement learning in vehicular networks,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Privacy-preserving byzantine-robust federated learning via deep reinforcement learning in vehicular networks,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94173464-615c-4d59-b885-0ee3194c6a58 · outbound

This paper cites Enhancing machine learning-based ids for vehicular networks by addressing ad- versarial attacks,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Enhancing machine learning-based ids for vehicular networks by addressing ad- versarial attacks,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec8c95cc-1a98-403a-b711-e1532380cd3a · outbound

This paper cites A comprehensive analysis of model poisoning attacks in federated learning for autonomous vehicles: A benchmark study,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A comprehensive analysis of model poisoning attacks in federated learning for autonomous vehicles: A benchmark study,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24597ab7-8bfd-4bf1-8043-314470535fef · outbound

This paper cites Targeted attacks and defenses for distributed federated learning in vehicular networks,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Targeted attacks and defenses for distributed federated learning in vehicular networks,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 440ee07c-4a27-4243-a964-8e03fcd18323 · outbound

This paper cites Inverting gradients-how easy is it to break privacy in federated learning?.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Inverting gradients-how easy is it to break privacy in federated learning?

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efb081f1-f241-44d5-8d85-107e42cddb9a · outbound

This paper cites A lightweight authentication and privacy-preserving aggregation for blockchain- enabled federated learning in vanets,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A lightweight authentication and privacy-preserving aggregation for blockchain- enabled federated learning in vanets,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 750e2847-890e-4fa6-b061-33d75abd1b98 · outbound

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

SoK: Federated Learning for Intrusion Detection in Vehicular Networks In-vehicle network intrusion detection using deep convolutional neural network,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cad39198-00bf-40ce-afe3-ce2d7d4bd4f3 · outbound

This paper cites Can-mirgu: a comprehensive can bus attack dataset from moving vehicles for intrusion detection system evaluation.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Can-mirgu: a comprehensive can bus attack dataset from moving vehicles for intrusion detection system evaluation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6408b7ca-c822-4b6c-b009-74fff60f1eb0 · outbound

This paper cites A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a98d925d-c986-4826-82d6-7ec6f57f6945 · outbound

This paper cites Canet: An unsupervised intrusion detection system for high dimensional can bus data,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Canet: An unsupervised intrusion detection system for high dimensional can bus data,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e5144c3-ac6d-4b7f-8885-8ae7ccacdfcc · outbound

This paper cites Can-train-and-test: A curated can dataset for automotive intrusion detection,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Can-train-and-test: A curated can dataset for automotive intrusion detection,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 291bdb73-7274-4ecb-977b-59f908bff367 · outbound

This paper cites Semi- supervised federated learning for misbehavior detection of bsms in ve- hicular networks,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Semi- supervised federated learning for misbehavior detection of bsms in ve- hicular networks,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f48886bb-3c69-47ef-a16a-c08fcc5fce43 · outbound

This paper cites Microscopic traffic simulation using sumo,.

SoK: Federated Learning for Intrusion Detection in Vehicular Networks Microscopic traffic simulation using sumo,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-14T08:20:27.345797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers