Detecting and responding to malicious acts directed towards machine learning models

Abstract

A system detects and responds to malicious acts directed towards machine learning models. Data fed into and output by a machine learning model is collected by a sensor. The data fed into the model includes vectorization data, which is generated from raw data provided from a requester, such as for example a stream of timeseries data. The output data may include a prediction or other output generated by the machine learning model in response to receiving the vectorization data. The vectorization data and machine learning model output data are processed to determine whether the machine learning model is being subject to a malicious act (e.g., attack). The output of the processing may indicate an attack score. A response for handling the request by a requester may be selected based on the output that includes the attack score, and the response may be applied to the requestor.