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US Patent 10083071 Temporal anomaly detection on automotive networks

Patent 10083071 was granted and assigned to Battelle Memorial Institute on September, 2018 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Battelle Memorial Institute
Battelle Memorial Institute
Current Assignee
Battelle Memorial Institute
Battelle Memorial Institute
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10083071
Patent Inventor Names
David Sherman0
Anuja Sonalker0
Date of Patent
September 25, 2018
Patent Application Number
14857098
Date Filed
September 17, 2015
Patent Citations Received
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US Patent 12028365 CAN bus protection systems and methods
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US Patent 11288060 Detecting anomalies online using historical controller processing activity
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US Patent 11689434 Network anomaly detection
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US Patent 11711384 Method and system for detecting message injection anomalies
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US Patent 11711386 Multi-state messenging anomaly detection for securing a broadcast network
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US Patent 11425005 Controller area network anomaly detection
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US Patent 10410002 Intrusion detection apparatus, system and methods
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US Patent 11526348 Detecting anomalies online using controller processing activity
...
Patent Primary Examiner
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Amine Riad
Patent abstract

An anomaly detector for a Controller Area Network (CAN) bus performs state space classification on a per-message basis of messages on the CAN bus to label messages as normal or anomalous, and performs temporal pattern analysis as a function of time to label unexpected temporal patterns as anomalous. The anomaly detector issues an alert if an alert criterion is met that is based on the outputs of the state space classification and the temporal pattern analysis. The temporal pattern analysis may compare statistics of messages having analyzed arbitration IDs with statistics for messages having those analyzed arbitration IDs in a training dataset of CAN bus messages, and a temporal pattern is anomalous if there is a statistically significant deviation from the training dataset. The anomaly detector may be implemented on a vehicle Electronic Control Unit (ECU) communicating via a vehicle CAN bus. The anomaly detector does not rely on an database of messages and their periodicity from manufacturers (dbc files) and in that sense is truly a zero knowledge detector.

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