A method for protection from cyber attacks in a CAN (Controller Area Network), of a vehicle including the steps of selecting periodic messages having a transmission periodicity, grouping the periodic messages, and performing an analysis of messages of the nodes that exchange the received periodic messages, which includes obtaining times of arrival at the respective nodes of a set of periodic messages that have the same message identifier, computing average-offset values over successive subsets, of a given number of messages, accumulating the average-offset values for each identifier to obtain accumulated-offset values, identifying linear parameters by computing an angular coefficient, of a regression, and an intercept, or identification error, computing a correlation coefficient of the average offset of pairs of messages identified as coming from the same node, determining whether the correlation coefficient is higher than a first given threshold, determining whether the angular coefficient between two consecutive messages with the same identifier is higher than a second given threshold, determining whether the intercept between two consecutive messages is higher than a third given threshold, and supplying the results of these determinations to a message-classification operation.
Method For Protection From Cyber Attacks To A Vehicle Based Upon Time Analysis, And Corresponding Device
Sergio Saponara;Alessio Gagliardi;Pierpaolo Dini
2021-01-01
Abstract
A method for protection from cyber attacks in a CAN (Controller Area Network), of a vehicle including the steps of selecting periodic messages having a transmission periodicity, grouping the periodic messages, and performing an analysis of messages of the nodes that exchange the received periodic messages, which includes obtaining times of arrival at the respective nodes of a set of periodic messages that have the same message identifier, computing average-offset values over successive subsets, of a given number of messages, accumulating the average-offset values for each identifier to obtain accumulated-offset values, identifying linear parameters by computing an angular coefficient, of a regression, and an intercept, or identification error, computing a correlation coefficient of the average offset of pairs of messages identified as coming from the same node, determining whether the correlation coefficient is higher than a first given threshold, determining whether the angular coefficient between two consecutive messages with the same identifier is higher than a second given threshold, determining whether the intercept between two consecutive messages is higher than a third given threshold, and supplying the results of these determinations to a message-classification operation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
