SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks | Paper Citation Record

Observation 1fec13f7-03ae-46d1-9933-f0c96b73d945 · outbound

This paper cites Countermeasures against various network attacks using machine learning methods,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Countermeasures against various network attacks using machine learning methods,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.381535Z

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Observation 6e3b30c0-24ad-40c0-aef7-13e41bcbefc5 · outbound

This paper cites Efficient data flow algorithms for autonomous lane changing, passing and overtaking behaviors,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Efficient data flow algorithms for autonomous lane changing, passing and overtaking behaviors,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.371167Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3077ab89-ef18-4f92-880b-d52dba5e8d68 · outbound

This paper cites A survey of vehicle to everything (v2x) testing,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks A survey of vehicle to everything (v2x) testing,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.360191Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation da4b5182-7c3e-4a0d-b9c4-b21a56ce6dd4 · outbound

This paper cites In-vehicle networking: Protocols, challenges, and solutions,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks In-vehicle networking: Protocols, challenges, and solutions,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.348781Z

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Observation dbde7f3b-2de4-4f19-96ef-5987a3603d24 · outbound

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

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks State-of-the-Art Survey on In-Vehicle Network Communication (CAN-Bus) Security and Vulnerabilities

Reference 5

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Observation 920914cb-536b-4131-a6ad-2777e35e6eed · outbound

This paper cites In-vehicle networks: Attacks, vulnerabilities, and proposed solutions,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks In-vehicle networks: Attacks, vulnerabilities, and proposed solutions,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.338295Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 16a89a50-a5a7-489d-aa63-c21b7d9be2d8 · outbound

This paper cites Software-defined networking: A comprehensive survey,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Software-defined networking: A comprehensive survey,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.327779Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a6f81fdf-0937-4caf-88a1-0b25c6cedca0 · outbound

This paper cites Cybersecurity attacks in vehicle-to-infrastructure applications and their prevention,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Cybersecurity attacks in vehicle-to-infrastructure applications and their prevention,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.317834Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation add643f8-e87a-4ba9-acc4-24673254b19d · outbound

This paper cites The 2015 ukraine blackout: Implications for false data injection attacks,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks The 2015 ukraine blackout: Implications for false data injection attacks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.307409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 17ba5133-f359-47b9-b8c4-fe2f1bc452b5 · outbound

This paper cites ML Attack Models: Adversarial Attacks and Data Poisoning Attacks.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks ML Attack Models: Adversarial Attacks and Data Poisoning Attacks

Reference 10

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Observation 83618e6a-3531-4c94-9731-2f8d058619d1 · outbound

This paper cites Explaining and harnessing adversarial examples,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Explaining and harnessing adversarial examples,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:20.647607Z

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Observation 1aad442c-e29c-4af5-bac6-7319e8e47c4b · outbound

This paper cites Adversarial examples in the physical world.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Adversarial examples in the physical world

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:20.651058Z

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Observation dba0ba1e-6871-4d3d-81e1-56df19f0a6e8 · outbound

This paper cites Deepfool: A simple and accurate method to fool deep neural networks,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Deepfool: A simple and accurate method to fool deep neural networks,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:20.654929Z

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Observation 5cbe9263-c4de-499a-a581-54ba02f8560b · outbound

This paper cites Investigating the impact of evasion attacks against automotive intrusion detection systems,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Investigating the impact of evasion attacks against automotive intrusion detection systems,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.285128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2c677f29-7112-400e-8e6d-fcaa68946cc2 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:20.661523Z

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Observation 04483c1d-8cd0-46af-bfb8-34760a3b9fd3 · outbound

This paper cites Adversarial examples are not bugs, they are features,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Adversarial examples are not bugs, they are features,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.275412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fe510f04-6595-496d-855d-b2abb4a6a619 · outbound

This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.265876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 92cb8dd2-042a-4f99-b76f-68679fc38d3c · outbound

This paper cites Robust machine learning against adversarial samples at test time,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Robust machine learning against adversarial samples at test time,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.256625Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4ba25560-467f-470d-82f3-48886cb40ec1 · outbound

This paper cites Smooth Adversarial Training.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Smooth Adversarial Training

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:20.674523Z

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Observation 952a1831-3fb1-4a58-954a-d94358cacc09 · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Theoretically principled trade-off between robustness and accuracy,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.247210Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4573f05b-e305-4128-90f5-2b62049b7dae · outbound

This paper cites An adversarial attack defending system for securing in-vehicle networks,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks An adversarial attack defending system for securing in-vehicle networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.237091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0551892f-adad-41f9-bfbc-3a7a0e7cb249 · outbound

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

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Otids: A novel intrusion detection system for in-vehicle network by using remote frame,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.227336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a185fdda-6fc1-4a6e-b293-6b3df74c302b · outbound

This paper cites In-vehicle network attacks and countermeasures: Challenges and future directions,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks In-vehicle network attacks and countermeasures: Challenges and future directions,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.217537Z

Source-reported events for the cited work

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Observation 48cb96a5-9a79-427c-a506-e0263734b0f8 · outbound

This paper cites A survey on security attacks and defense techniques for connected and autonomous vehicles,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks A survey on security attacks and defense techniques for connected and autonomous vehicles,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.207587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation eb40c3d7-546b-4d3d-a2a7-e6e6ccc48688 · outbound

This paper cites A structured approach to anomaly detection for in-vehicle networks,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks A structured approach to anomaly detection for in-vehicle networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.197238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8320bd5b-b5e7-41b6-9f0a-04b1c08a7fbb · outbound

This paper cites Entropy-based anomaly detection for in-vehicle networks,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Entropy-based anomaly detection for in-vehicle networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.187281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5eb4e82a-fc1b-44e7-80ad-41f1664d1c2c · outbound

This paper cites Potential sources of sensor data anomalies for autonomous vehicles: An overview from road vehicle safety perspective,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Potential sources of sensor data anomalies for autonomous vehicles: An overview from road vehicle safety perspective,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.176974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9e3ad1f1-eb65-4f45-b8cf-99895194befa · outbound

This paper cites Anomaly detection in connected and autonomous vehicles: A survey, analysis, and research challenges,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Anomaly detection in connected and autonomous vehicles: A survey, analysis, and research challenges,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.071672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6c474b6c-ecc3-440f-ad07-471e2b3a47c5 · outbound

This paper cites Anomaly diagnosis of connected autonomous vehicles: A survey,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Anomaly diagnosis of connected autonomous vehicles: A survey,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.061823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8c2ca8de-7b25-47d3-9143-64afdbda5e2a · outbound

This paper cites Fingerprinting electronic control units for vehicle intrusion detection,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Fingerprinting electronic control units for vehicle intrusion detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.052088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f7dde5f0-fdab-4903-b16d-8943c86631ac · outbound

This paper cites Intrusion detection system using deep neural network for in-vehicle network security,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Intrusion detection system using deep neural network for in-vehicle network security,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.041179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0b158588-b66c-4f7f-a998-e207f5f33764 · outbound

This paper cites Supervised and unsupervised intrusion detection based on can message frequencies for in-vehicle network,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Supervised and unsupervised intrusion detection based on can message frequencies for in-vehicle network,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.030539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c951f7f6-ae70-4fb5-945c-1038e38b0404 · outbound

This paper cites False data injection attacks against state estimation in electric power grids,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks False data injection attacks against state estimation in electric power grids,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:21.010570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b4f82804-56d5-4551-b398-346c32cadb26 · outbound

This paper cites A comprehensive survey of false data injection in smart grid,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks A comprehensive survey of false data injection in smart grid,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:20.992584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9942fe6d-87f3-4f7a-a434-890f6e7a451c · outbound

This paper cites False data injection attack and its countermeasures in wireless sensor networks,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks False data injection attack and its countermeasures in wireless sensor networks,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:20.980858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cc619eb9-ccc7-419e-980c-cc68b8771935 · outbound

This paper cites Proof-of-relevance: Filtering false data via authentic consensus in vehicle ad-hoc networks,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Proof-of-relevance: Filtering false data via authentic consensus in vehicle ad-hoc networks,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:20.969026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0fc2c63f-c17f-466b-9812-803fd212cbee · outbound

This paper cites Modeling inter-signal arrival times for accurate detection of can bus signal injection attacks: a data-driven approach to in-vehicle intrusion detection,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Modeling inter-signal arrival times for accurate detection of can bus signal injection attacks: a data-driven approach to in-vehicle intrusion detection,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:20.945290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 355be4f8-d392-46f1-a609-297005f1a5bb · outbound

This paper cites SDVN: enabling rapid network innovation for heterogeneous vehicular communication,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks SDVN: enabling rapid network innovation for heterogeneous vehicular communication,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:20.931204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 54ff1527-0fbb-453c-8555-1921f6bbdcd8 · outbound

This paper cites Ml-based approach to detect ddos attack in v2i communication under sdn architecture,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Ml-based approach to detect ddos attack in v2i communication under sdn architecture,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:20.920518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 00b82407-58d0-480b-8454-6b1c1321747f · outbound

This paper cites Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network

Reference 40

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Source-reported events for the cited work

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Observation eda18fa7-cda6-4bcb-8aa5-80fc2084b420 · outbound

This paper cites OpenDBC,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks OpenDBC,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:20.910155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f7d10a4b-31f5-445c-be3f-b59b6d5ca65e · outbound

This paper cites Goodfellow, Y.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Goodfellow, Y

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:20.752320Z

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Observation bf5dd803-7966-4147-b0c9-a2458682644a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Adam: A Method for Stochastic Optimization

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:20.755625Z

Source-reported events for the cited work

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Observation 1317784b-9310-41f4-82be-04629bdb95d9 · outbound

This paper cites GENI: A federated testbed for innovative network experiments,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks GENI: A federated testbed for innovative network experiments,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:20.893159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 69803c5e-8c97-4821-a041-a6042b612bb8 · outbound

This paper cites Project Floodlight,.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Project Floodlight,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:58:20.882367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fe047785-449a-4bba-8fc8-9cf6eb721425 · outbound

This paper cites Adversarial Robustness Toolbox v1.0.0.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks Adversarial Robustness Toolbox v1.0.0

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:20.766497Z

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