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...
Saved in:
| Date: | 2026 |
|---|---|
| Main Authors: | , |
| Format: | Article |
| Language: | Ukrainian |
| Published: |
Інститут прикладних проблем механіки і математики ім. Я. С. Підстригача НАН України
2026
|
| Subjects: | |
| Online Access: | https://www.fmmit.lviv.ua/index.php/fmmit/article/view/439 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Journal Title: | Physico-mathematical modeling and informational technologies |
| Download file: | |
Institution
Physico-mathematical modeling and informational technologies| Summary: | 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. |
|---|---|
| DOI: | 10.15407/fmmit2026.42.119 |