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dc.contributor.advisorStarobinski, Daviden_US
dc.contributor.advisorPetit, Jonathanen_US
dc.contributor.authorSo, Steven Rhejohn Barlinen_US
dc.date.accessioned2019-06-26T14:59:15Z
dc.date.available2019-06-26T14:59:15Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/2144/36073
dc.description.abstractThe safety and efficiency of vehicular communications rely on the correctness of the data exchanged between vehicles. Location spoofing is a proven and powerful attack against Vehicle-to-everything (V2X) communication systems that can cause traffic congestion and other safety hazards. Recent work also demonstrates practical spoofing attacks that can confuse intelligent transportation systems at road intersections. In this work, we propose two self-reliant schemes at the application layer and the physical layer to detect such misbehaviors. These schemes can be run independently by each vehicle and do not rely on the assumption that the majority of vehicles is honest. We first propose a scheme that uses application-layer plausibility checks as a feature vector for machine learning models. Our results show that this scheme improves the precision of the plausibility checks by over 20% by using them as feature vectors in KNN and SVM classifiers. We also show how to classify different types of known misbehaviors, once they are detected. We then propose three novel physical layer plausibility checks that leverage the received signal strength indicator (RSSI) of basic safety messages (BSMs). These plausibility checks have multi-step mechanisms to improve not only the detection rate, but also to decrease false positives. We comprehensively evaluate the performance of these plausibility checks using the VeReMi dataset (which we enhance along the way) for several types of attacks. We show that the best performing physical layer plausibility check among the three considered achieves an overall detection rate of 83.73% and a precision of 95.91%. The proposed application-layer and physical-layer plausibility checks provide a promising framework toward the deployment of on self-reliant misbehavior detection systems.en_US
dc.language.isoen_US
dc.subjectComputer engineeringen_US
dc.subjectConnected vehiclesen_US
dc.subjectMisbehavior detectionen_US
dc.subjectSecurityen_US
dc.subjectV2Xen_US
dc.subjectVANETen_US
dc.titleSelf-reliant misbehavior detection in V2X networksen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2019-06-04T01:06:54Z
etd.degree.nameMaster of Scienceen_US
etd.degree.levelmastersen_US
etd.degree.disciplineElectrical & Computer Engineeringen_US
etd.degree.grantorBoston Universityen_US


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