Threat detection using machine learning query analysis

Abstract

Within an organization, numerous different persons can access data. But a user account with database access may be compromised, leading to data theft and data destruction. Database queries used to access data may vary in length, content, and formatting. Features of these queries can be extracted to train a machine learning classifier. Queries for users can be mapped to a vector space and when a new sample query is received, it can be assessed using the classifier to determine its level of similarity with previous queries by that user and other users. By analyzing the results of this assessment on the new query, it can be determined if this new query represents a data access anomaly—e.g. a particularly unusual query for a user, given his or her past, that may indicate user credentials have been compromised. When a data access anomaly exists, a remedial action may be take.