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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Datum:2026
Hauptverfasser: Дзелендзяк, Павло, Наконечний, Ростислав
Format: Artikel
Sprache:Ukrainisch
Veröffentlicht: Інститут прикладних проблем механіки і математики ім. Я. С. Підстригача НАН України 2026
Schlagworte:
Online Zugang:https://www.fmmit.lviv.ua/index.php/fmmit/article/view/439
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Назва журналу:Physico-mathematical modeling and informational technologies
Завантажити файл: Pdf

Institution

Physico-mathematical modeling and informational technologies
Beschreibung
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.
DOI:10.15407/fmmit2026.42.119