Detecting Message Modification Attacks on the CAN Bus with Temporal Convolutional Networks Article Swipe
Irina Chiscop
,
András Gazdag
,
Joost Bosman
,
Gergely Biczók
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5220/0010445504880496
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5220/0010445504880496
Multiple attacks have shown that in-vehicle networks have vulnerabilities\nwhich can be exploited. Securing the Controller Area Network (CAN) for modern\nvehicles has become a necessary task for car manufacturers. Some attacks inject\npotentially large amount of fake messages into the CAN network; however, such\nattacks are relatively easy to detect. In more sophisticated attacks, the\noriginal messages are modified, making the detection a more complex problem. In\nthis paper, we present a novel machine learning based intrusion detection\nmethod for CAN networks. We focus on detecting message modification attacks,\nwhich do not change the timing patterns of communications. Our proposed\ntemporal convolutional network-based solution can learn the normal behavior of\nCAN signals and differentiate them from malicious ones. The method is evaluated\non multiple CAN-bus message IDs from two public datasets including different\ntypes of attacks. Performance results show that our lightweight approach\ncompares favorably to the state-of-the-art unsupervised learning approach,\nachieving similar or better accuracy for a wide range of scenarios with a\nsignificantly lower false positive rate.\n
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5220/0010445504880496
- OA Status
- hybrid
- Cited By
- 3
- References
- 23
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W3160093313Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5220/0010445504880496Digital Object Identifier
- Title
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Detecting Message Modification Attacks on the CAN Bus with Temporal Convolutional NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-01-01Full publication date if available
- Authors
-
Irina Chiscop, András Gazdag, Joost Bosman, Gergely BiczókList of authors in order
- Landing page
-
https://doi.org/10.5220/0010445504880496Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.5220/0010445504880496Direct OA link when available
- Concepts
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Computer science, Intrusion detection system, Focus (optics), Task (project management), CAN bus, State (computer science), Convolutional neural network, False positive rate, Computer network, Artificial intelligence, Real-time computing, Machine learning, Physics, Optics, Algorithm, Management, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2022: 1Per-year citation counts (last 5 years)
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23Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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