Abstract
In view of system reliability, extraction of knowledge from models of artificial intelligence may be more important than their forecasting ability. The elaboration of rules found by explainable artificial intelligence gives here insight into the problem of packet falsification in vehicle platooning. Detection and countermeasure are designed on the basis of feature and value ranking as well as rule confidence and they are validated under a large range of working conditions. The certification of safe operating conditions is found by achieving (statistically) zero false negatives, namely, the operating conditions predicted as ‘safe’ never lead to collision despite the cyber attack.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
