公开(公告)号：US20220407880A1

公开(公告)日：2022-12-22

申请号：US17804010

申请日：2022-05-25

申请人： Marelli Europe S.p.A.

发明人： Christian Rosadini ,  Anastasia Cornelio ,  Walter Nesci ,  Sergio Saponara ,  Alessio Gagliardi ,  Paola De Cesare

IPC分类号： H04L9/40

摘要： A method for protection from cyber attacks in a communication network of a vehicle comprising: the steps of building sets of dominant voltage measurements for each message identifier associated to a message that is passing; extracting statistical features; supplying the statistical features for each message identifier that are available at each instant at input to a neural network of a pattern-recognition type; carrying out an operation of classification, or pattern recognition, supplying a prediction of a membership class corresponding to a given node on the basis of the statistical features supplied at input; evaluating whether the prediction supplied by the neural network corresponds to a given node that allows as admissible message identifier the message identifier at input and, if it does not, signalling an anomaly for the message identifier; and evaluating whether a number of anomalies signalled for said message identifier exceeds a given threshold.

公开(公告)号：US12052275B2

公开(公告)日：2024-07-30

申请号：US17804010

申请日：2022-05-25

申请人： Marelli Europe S.p.A.

发明人： Christian Rosadini ,  Anastasia Cornelio ,  Walter Nesci ,  Sergio Saponara ,  Alessio Gagliardi ,  Paola De Cesare

IPC分类号： H04L29/06 ,  H04L9/40

CPC分类号： H04L63/1425

摘要： A method for protection from cyber attacks in a communication network of a vehicle comprising: the steps of building sets of dominant voltage measurements for each message identifier associated to a message that is passing; extracting statistical features; supplying the statistical features for each message identifier that are available at each instant at input to a neural network of a pattern-recognition type; carrying out an operation of classification, or pattern recognition, supplying a prediction of a membership class corresponding to a given node on the basis of the statistical features supplied at input; evaluating whether the prediction supplied by the neural network corresponds to a given node that allows as admissible message identifier the message identifier at input and, if it does not, signalling an anomaly for the message identifier; and evaluating whether a number of anomalies signalled for said message identifier exceeds a given threshold.