Розроблення та верифікація програмно-апаратного середовища для виявлення та візуалізації помилок у мережах CAN
This paper presents the development of a software environment for simulation, detection, and visualization of errors in Controller Area Network (CAN) systems based on the Flutter framework. Theproposedapproachintroducesanextendedobject-orienteddatamodelwithembeddedGroundTruthlabels, anabling control...
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| Datum: | 2026 |
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| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | Ukrainisch |
| Veröffentlicht: |
Інститут прикладних проблем механіки і математики ім. Я. С. Підстригача НАН України
2026
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| Online Zugang: | https://www.fmmit.lviv.ua/index.php/fmmit/article/view/439 |
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| Назва журналу: | Physico-mathematical modeling and informational technologies |
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Physico-mathematical modeling and informational technologies| Zusammenfassung: | This paper presents the development of a software environment for simulation, detection, and visualization of errors in Controller Area Network (CAN) systems based on the Flutter framework. Theproposedapproachintroducesanextendedobject-orienteddatamodelwithembeddedGroundTruthlabels, anabling controlled injection offiveanomaly types combinedwithautomatedcalculationofdetectionaccuracy metrics. Thedevelopedsystemimplementsfivefaultinjectionscenarios: bit-levelmutations, bursterrors, staticpatterninjection, byteorderviolations, and CRC faults. To evaluate detection algorithm performance, a Confusion Matrix is computed in real time, providing Precision, Recall, and F1-Score metrics. Local data persistence is implemented using SQLite, while the system architecture follows a strict separation between processing logic and the user interface, leveraging Dart's asynchronous mechanisms. Practical testing on a dataset of 65 frames confirmed the system's operability and identified directions for further improvement of detection algorithms. Thedevelopedtoolsetcanserveas a benchmarkingplatformfornovelintrusiondetectionmethodsincybersecuritysystemsofmodernautomotivesystems. |
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| DOI: | 10.15407/fmmit2026.42.119 |