Du har inte javascript påslaget. Det innebär att många funktioner inte fungerar. För mer information om Vinnova, ta kontakt med oss.

Våra e-tjänster för ansökningar, projekt och bedömningar stänger torsdagen den 8 maj kl 16:30 för systemuppdateringar. De beräknas öppna igen senast fredagen den 9 maj kl 8:00.

CyReV(fas2)-Datasäkerhet för fordonssystem i en föränderlig miljö

Diarienummer
Koordinator Volvo Technology AB - Volvo GTT
Bidrag från Vinnova 10 098 287 kronor
Projektets löptid oktober 2019 - december 2023
Status Avslutat
Utlysning Elektronik, mjukvara och kommunikation - FFI
Ansökningsomgång Elektronik, mjukvara och kommunikation - FFI - 2019-06-11

Viktiga resultat som projektet gav

Address the problem of how to detect and react to security incidents in vehicular systems. Conduct research on reactive measures when detecting potential security incidents. Develop methods for the design of resilient systems. Identify what means are necessary to enable post-event analysis and find out the reasons as to why and how an intrusion has happened. Tools and mechanisms useful in resilient designs were identified. Literature reviews were performed. Useful safety handling mechanisms were identified. Pre-injection analyses were evaluated.

Långsiktiga effekter som förväntas

Insight into the state of current research frontiers in security and resiliency for vehicles and vehicular security was gained. Reference architectures and frameworks have been designed. Knowledge gained about how to react when security events are detected. Systematic literature reviews performed. Analysis of IDS systems and how to react when anomalies are detected. Intrusion detection within the vehicle was analysed. Six handling mechanisms were analysed.

Upplägg och genomförande

Interview study performed. Resilience reference architecture developed and tested with respect to performance and usability. Data-driven models for anomaly and intrusion detection developed. Selected existing technologies from automotive and other fields analysed. Interplay analysis based on layered resilience framework performed. Data model for post-attack forensics was developed. Live forensics techniques based on machine learning developed. Pre-injection analysis for model-implemented fault and attack injection investigated.

Externa länkar

Texten på den här sidan har projektgruppen själv formulerat. Innehållet är inte granskat av våra redaktörer.

Senast uppdaterad 3 januari 2025

Diarienummer 2019-03071