| CPC H04L 43/067 (2013.01) [G06F 18/214 (2023.01); G06F 18/2321 (2023.01); G07C 5/0808 (2013.01); G07C 5/0816 (2013.01); H04L 43/04 (2013.01); H04L 43/0817 (2013.01)] | 18 Claims |
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1. A system for detecting vehicle data volume anomalies, the system comprising:
at least one datastore operable to store vehicle data originating from a plurality of monitoring devices installed in a plurality of vehicles;
at least one processor in communication with the at least one data store, the at least one processor operable to:
receive, during a training period, a first collection of vehicle data from the plurality of monitoring devices;
train a machine learning model using the first collection of vehicle data to forecast a future amount of vehicle data to be received from the plurality of monitoring devices;
determine, based on the machine learning model, a plurality of confidence intervals for the predicted future amount of vehicle data;
determine that no confidence interval satisfies a percentile width of the first collection of vehicle data;
iteratively retrain the machine learning model using a subset of the first collection of vehicle data until at least one of the confidence intervals satisfies the percentile width of the first collection of vehicle data;
select one of the confidence intervals that satisfies the percentile width of the first collection of vehicle data;
receive, during a forecasting period, a second collection of vehicle data from the plurality of monitoring devices;
detect a vehicle data volume anomaly in the second collection of vehicle data by determining that the amount of vehicle data in the second collection of vehicle data is outside of the selected confidence interval; and
generate a notification indicating the detection of the vehicle data volume anomaly.
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