Context Dependent V2X Misbehavior Detection

    公开(公告)号:US20220256347A1

    公开(公告)日:2022-08-11

    申请号:US17171822

    申请日:2021-02-09

    Abstract: Methods, apparatuses, systems, and non-transitory computer-readable media are disclosed for V2X misbehavior detection at a device. A disclosed method comprises performing context detection to generate a determined context for the device. The method further comprises performing a plurality of plausibility checks to generate a plurality of plausibility outputs. At least one plausibility check of the plurality of plausibility checks is performed based on inputs including (1) a reported value obtained from a received V2X message and (2) the determined context for the device. The method further comprises weighing and combining the plurality of plausibility outputs, by applying at least one set of weights based on the determined context for the device, to generate at least one combined, weighted plausibility indicator value. The method further comprises performing at least one misbehavior detection based on the at least one combined, weighted plausibility indicator value, to generate at least one misbehavior detection result.

    Misbehavior Indication Aggregators For Identifying Misbehavior Conditions In Vehicle-To-Everything (V2X) Communication Systems

    公开(公告)号:US20230345249A1

    公开(公告)日:2023-10-26

    申请号:US17660513

    申请日:2022-04-25

    CPC classification number: H04W12/122 H04W4/40

    Abstract: Embodiment methods implemented in vehicle-to-everything (V2X) systems may include detecting multiple indications of V2X misbehavior by multiple misbehavior detection mechanisms, aggregating misbehavior indications output from the misbehavior detection mechanisms, determining whether a reportable or actionable misbehavior condition exists based on aggregated misbehavior indications, and acting on a determined misbehavior condition. Aggregating misbehavior indications may include determining whether any of the misbehavior detection mechanisms outputs a misbehavior indication, determining whether any one of a select subset of the one or more misbehavior detection mechanisms outputs a misbehavior indication, aggregating misbehavior indications output from multiple misbehavior detection mechanisms and comparing the number to a threshold, applying a weight to misbehavior indication outputs and aggregating the weighted outputs, or determining a number of events classified as an attack or misbehavior indications output by each of a plurality of misbehavior detection mechanisms within a window of time or set number of events.

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