"M-CAN Intrusion Detection Dataset" Article Swipe
"This dataset contains Controller Area Network (CAN) traffic collected from the M-CAN bus of a Genesis G80 vehicle. M-CAN is a mid-speed bus responsible for communication with navigation systems, multimedia devices, and related in-vehicle modules. Approximately 36 minutes of normal CAN traffic were extracted during real driving, and attack datasets were generated by injecting artificial DoS and Fuzzing messages into the normal traces. Each CAN frame includes timestamps, CAN identifiers, DLC values, payload bytes, and a binary label indicating normal or injected traffic. This dataset enables research on intrusion detection, CAN-bus anomaly behavior, and security analysis of multimedia-related automotive networks."
Related Topics
Concepts
Intrusion detection system
Computer science
Payload (computing)
Frame (networking)
Real-time computing
Anomaly detection
Intrusion
CAN bus
Computer network
Intrusion prevention system
Global Positioning System
Anomaly (physics)
Controller (irrigation)
Data mining
Traffic analysis
Binary number
Class (philosophy)
Evasion (ethics)
Artificial intelligence
Network security
Filter (signal processing)
Computer vision
Telecommunications network
Engineering
Metadata
- Type
- dataset
- Landing Page
- https://doi.org/10.21227/mkvd-h021
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7106809658
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W7106809658Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21227/mkvd-h021Digital Object Identifier
- Title
-
"M-CAN Intrusion Detection Dataset"Work title
- Type
-
datasetOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-26Full publication date if available
- Authors
-
Hwejae Lee, Huy Kang KimList of authors in order
- Landing page
-
https://doi.org/10.21227/mkvd-h021Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.21227/mkvd-h021Direct OA link when available
- Concepts
-
Intrusion detection system, Computer science, Payload (computing), Frame (networking), Real-time computing, Anomaly detection, Intrusion, CAN bus, Computer network, Intrusion prevention system, Global Positioning System, Anomaly (physics), Controller (irrigation), Data mining, Traffic analysis, Binary number, Class (philosophy), Evasion (ethics), Artificial intelligence, Network security, Filter (signal processing), Computer vision, Telecommunications network, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
Full payload
| id | https://openalex.org/W7106809658 |
|---|---|
| doi | https://doi.org/10.21227/mkvd-h021 |
| ids.doi | https://doi.org/10.21227/mkvd-h021 |
| ids.openalex | https://openalex.org/W7106809658 |
| fwci | |
| type | dataset |
| title | "M-CAN Intrusion Detection Dataset" |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C35525427 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7108937501907349 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q745881 |
| concepts[0].display_name | Intrusion detection system |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.613361656665802 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C134066672 |
| concepts[2].level | 3 |
| concepts[2].score | 0.6002721190452576 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1424639 |
| concepts[2].display_name | Payload (computing) |
| concepts[3].id | https://openalex.org/C126042441 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5322453379631042 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1324888 |
| concepts[3].display_name | Frame (networking) |
| concepts[4].id | https://openalex.org/C79403827 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5177299380302429 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3988 |
| concepts[4].display_name | Real-time computing |
| concepts[5].id | https://openalex.org/C739882 |
| concepts[5].level | 2 |
| concepts[5].score | 0.49291208386421204 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q3560506 |
| concepts[5].display_name | Anomaly detection |
| concepts[6].id | https://openalex.org/C158251709 |
| concepts[6].level | 2 |
| concepts[6].score | 0.49200791120529175 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q354025 |
| concepts[6].display_name | Intrusion |
| concepts[7].id | https://openalex.org/C201762086 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4748166501522064 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q728183 |
| concepts[7].display_name | CAN bus |
| concepts[8].id | https://openalex.org/C31258907 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4128906726837158 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[8].display_name | Computer network |
| concepts[9].id | https://openalex.org/C27061796 |
| concepts[9].level | 3 |
| concepts[9].score | 0.35157740116119385 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q745881 |
| concepts[9].display_name | Intrusion prevention system |
| concepts[10].id | https://openalex.org/C60229501 |
| concepts[10].level | 2 |
| concepts[10].score | 0.3448253571987152 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q18822 |
| concepts[10].display_name | Global Positioning System |
| concepts[11].id | https://openalex.org/C12997251 |
| concepts[11].level | 2 |
| concepts[11].score | 0.3330361247062683 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q567560 |
| concepts[11].display_name | Anomaly (physics) |
| concepts[12].id | https://openalex.org/C203479927 |
| concepts[12].level | 2 |
| concepts[12].score | 0.31976622343063354 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q5165939 |
| concepts[12].display_name | Controller (irrigation) |
| concepts[13].id | https://openalex.org/C124101348 |
| concepts[13].level | 1 |
| concepts[13].score | 0.31009751558303833 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[13].display_name | Data mining |
| concepts[14].id | https://openalex.org/C2781317605 |
| concepts[14].level | 2 |
| concepts[14].score | 0.30038514733314514 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7832483 |
| concepts[14].display_name | Traffic analysis |
| concepts[15].id | https://openalex.org/C48372109 |
| concepts[15].level | 2 |
| concepts[15].score | 0.29208651185035706 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q3913 |
| concepts[15].display_name | Binary number |
| concepts[16].id | https://openalex.org/C2777212361 |
| concepts[16].level | 2 |
| concepts[16].score | 0.2911505103111267 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q5127848 |
| concepts[16].display_name | Class (philosophy) |
| concepts[17].id | https://openalex.org/C2781251061 |
| concepts[17].level | 3 |
| concepts[17].score | 0.2887040376663208 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q5416089 |
| concepts[17].display_name | Evasion (ethics) |
| concepts[18].id | https://openalex.org/C154945302 |
| concepts[18].level | 1 |
| concepts[18].score | 0.2850809097290039 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[18].display_name | Artificial intelligence |
| concepts[19].id | https://openalex.org/C182590292 |
| concepts[19].level | 2 |
| concepts[19].score | 0.2833462357521057 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q989632 |
| concepts[19].display_name | Network security |
| concepts[20].id | https://openalex.org/C106131492 |
| concepts[20].level | 2 |
| concepts[20].score | 0.270505428314209 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q3072260 |
| concepts[20].display_name | Filter (signal processing) |
| concepts[21].id | https://openalex.org/C31972630 |
| concepts[21].level | 1 |
| concepts[21].score | 0.26199713349342346 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[21].display_name | Computer vision |
| concepts[22].id | https://openalex.org/C192126672 |
| concepts[22].level | 2 |
| concepts[22].score | 0.25160884857177734 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q1068715 |
| concepts[22].display_name | Telecommunications network |
| concepts[23].id | https://openalex.org/C127413603 |
| concepts[23].level | 0 |
| concepts[23].score | 0.25121167302131653 |
| concepts[23].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[23].display_name | Engineering |
| keywords[0].id | https://openalex.org/keywords/intrusion-detection-system |
| keywords[0].score | 0.7108937501907349 |
| keywords[0].display_name | Intrusion detection system |
| keywords[1].id | https://openalex.org/keywords/payload |
| keywords[1].score | 0.6002721190452576 |
| keywords[1].display_name | Payload (computing) |
| keywords[2].id | https://openalex.org/keywords/frame |
| keywords[2].score | 0.5322453379631042 |
| keywords[2].display_name | Frame (networking) |
| keywords[3].id | https://openalex.org/keywords/anomaly-detection |
| keywords[3].score | 0.49291208386421204 |
| keywords[3].display_name | Anomaly detection |
| keywords[4].id | https://openalex.org/keywords/intrusion |
| keywords[4].score | 0.49200791120529175 |
| keywords[4].display_name | Intrusion |
| keywords[5].id | https://openalex.org/keywords/can-bus |
| keywords[5].score | 0.4748166501522064 |
| keywords[5].display_name | CAN bus |
| keywords[6].id | https://openalex.org/keywords/intrusion-prevention-system |
| keywords[6].score | 0.35157740116119385 |
| keywords[6].display_name | Intrusion prevention system |
| keywords[7].id | https://openalex.org/keywords/global-positioning-system |
| keywords[7].score | 0.3448253571987152 |
| keywords[7].display_name | Global Positioning System |
| language | |
| locations[0].id | doi:10.21227/mkvd-h021 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S7407051695 |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | IEEE DataPort |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | |
| locations[0].raw_type | dataset |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | False |
| locations[0].is_published | |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.21227/mkvd-h021 |
| indexed_in | datacite |
| authorships[0].author.id | https://openalex.org/A2319113068 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-8070-3907 |
| authorships[0].author.display_name | Hwejae Lee |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hwejae Lee |
| authorships[0].is_corresponding | True |
| authorships[1].author.id | https://openalex.org/A2167578695 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0760-8807 |
| authorships[1].author.display_name | Huy Kang Kim |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Huy Kang Kim |
| authorships[1].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.21227/mkvd-h021 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-11-28T00:00:00 |
| display_name | "M-CAN Intrusion Detection Dataset" |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-28T02:12:24.556248 |
| primary_topic | |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.21227/mkvd-h021 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S7407051695 |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | IEEE DataPort |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | |
| best_oa_location.raw_type | dataset |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.21227/mkvd-h021 |
| primary_location.id | doi:10.21227/mkvd-h021 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S7407051695 |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | IEEE DataPort |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | |
| primary_location.raw_type | dataset |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.21227/mkvd-h021 |
| publication_date | 2025-11-26 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 14, 20, 75 |
| abstract_inverted_index.36 | 36 |
| abstract_inverted_index.by | 52 |
| abstract_inverted_index.is | 19 |
| abstract_inverted_index.of | 13, 38, 96 |
| abstract_inverted_index.on | 87 |
| abstract_inverted_index.or | 80 |
| abstract_inverted_index.CAN | 40, 64, 68 |
| abstract_inverted_index.DLC | 70 |
| abstract_inverted_index.DoS | 55 |
| abstract_inverted_index.G80 | 16 |
| abstract_inverted_index.and | 31, 47, 56, 74, 93 |
| abstract_inverted_index.bus | 12, 22 |
| abstract_inverted_index.for | 24 |
| abstract_inverted_index.the | 10, 60 |
| abstract_inverted_index.Area | 4 |
| abstract_inverted_index.Each | 63 |
| abstract_inverted_index.This | 83 |
| abstract_inverted_index.from | 9 |
| abstract_inverted_index.into | 59 |
| abstract_inverted_index.real | 45 |
| abstract_inverted_index.were | 42, 50 |
| abstract_inverted_index.with | 26 |
| abstract_inverted_index."This | 0 |
| abstract_inverted_index.(CAN) | 6 |
| abstract_inverted_index.M-CAN | 11, 18 |
| abstract_inverted_index.frame | 65 |
| abstract_inverted_index.label | 77 |
| abstract_inverted_index.attack | 48 |
| abstract_inverted_index.binary | 76 |
| abstract_inverted_index.bytes, | 73 |
| abstract_inverted_index.during | 44 |
| abstract_inverted_index.normal | 39, 61, 79 |
| abstract_inverted_index.CAN-bus | 90 |
| abstract_inverted_index.Fuzzing | 57 |
| abstract_inverted_index.Genesis | 15 |
| abstract_inverted_index.Network | 5 |
| abstract_inverted_index.anomaly | 91 |
| abstract_inverted_index.dataset | 1, 84 |
| abstract_inverted_index.enables | 85 |
| abstract_inverted_index.minutes | 37 |
| abstract_inverted_index.payload | 72 |
| abstract_inverted_index.related | 32 |
| abstract_inverted_index.traces. | 62 |
| abstract_inverted_index.traffic | 7, 41 |
| abstract_inverted_index.values, | 71 |
| abstract_inverted_index.analysis | 95 |
| abstract_inverted_index.contains | 2 |
| abstract_inverted_index.datasets | 49 |
| abstract_inverted_index.devices, | 30 |
| abstract_inverted_index.driving, | 46 |
| abstract_inverted_index.includes | 66 |
| abstract_inverted_index.injected | 81 |
| abstract_inverted_index.messages | 58 |
| abstract_inverted_index.modules. | 34 |
| abstract_inverted_index.research | 86 |
| abstract_inverted_index.security | 94 |
| abstract_inverted_index.systems, | 28 |
| abstract_inverted_index.traffic. | 82 |
| abstract_inverted_index.vehicle. | 17 |
| abstract_inverted_index.behavior, | 92 |
| abstract_inverted_index.collected | 8 |
| abstract_inverted_index.extracted | 43 |
| abstract_inverted_index.generated | 51 |
| abstract_inverted_index.injecting | 53 |
| abstract_inverted_index.intrusion | 88 |
| abstract_inverted_index.mid-speed | 21 |
| abstract_inverted_index.Controller | 3 |
| abstract_inverted_index.artificial | 54 |
| abstract_inverted_index.automotive | 98 |
| abstract_inverted_index.detection, | 89 |
| abstract_inverted_index.in-vehicle | 33 |
| abstract_inverted_index.indicating | 78 |
| abstract_inverted_index.multimedia | 29 |
| abstract_inverted_index.navigation | 27 |
| abstract_inverted_index.networks." | 99 |
| abstract_inverted_index.responsible | 23 |
| abstract_inverted_index.timestamps, | 67 |
| abstract_inverted_index.identifiers, | 69 |
| abstract_inverted_index.Approximately | 35 |
| abstract_inverted_index.communication | 25 |
| abstract_inverted_index.multimedia-related | 97 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |