Predicting data tampering using augmented machine learning models

A computer-implemented method comprising:

• receiving, by a server computer, a request from a target entity to modify event data for a historical event, of a plurality of historical events associated with a plurality of target entities stored to a database, the request comprising information about the target entity and information about the historical event;
• generating, by the server computer, a first security assessment score by, at least, applying a first set of machine learning models to the information about the target entity obtained from the request, the information about the historical event, and information about the target entity obtained from the database;
• retrieving, by the server computer from the database, event data associated with prior requests made by the target entity;
• computing, by the server computer, a second security assessment score by at least applying a second machine learning model to the retrieved event data associated with the prior requests made by the target entity, wherein the second machine learning model has been trained using labeled training data of the event data, and wherein the second machine learning model is augmented with an optimization model that has been trained using unlabeled training data of the event data;
• generating, by the server computer, an overall security assessment score for the request based on the first security assessment score and the second security assessment score;
• and
• providing, by the server computer, the overall security assessment score for the request to a client computer, wherein the overall security assessment score is usable by the client computer for preventing the target entity from accessing a resource.

A non-transitory computer-readable medium storing program code executable by one or more processing devices, wherein the program code, when executed by the one or more processing devices, configures the one or more processing devices to perform operations comprising:

• receiving a request from a target entity to modify event data for a historical event, of a plurality of historical events associated with a plurality of target entities stored to a database, the request comprising information about the target entity and information about the historical event;
• generating a first security assessment score by, at least, applying a first set of machine learning models to the information about the target entity obtained from the request, the information about the historical event, and information about the target entity obtained from the database;
• retrieving, from the database, event data associated with prior requests made by the target entity;
• computing a second security assessment score by, at least, applying a second machine learning model to the retrieved event data associated with the prior requests made by the target entity, wherein the second machine learning model has been trained using labeled training data of the event data, and wherein the second machine learning model is augmented with an optimization model that has been trained using unlabeled training data of the event data;
• generating an overall security assessment score for the request based on the first security assessment score and the second security assessment score;
• and
• providing the overall security assessment score for the request to a client computer.