Paypal, Inc.

Liron Ben Kimon

Yuri Shafet

David Tolpin

Michael Dymshits

Benjamin Hillel Myara

Eli Strajnik

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.

Threat detection using machine learning query analysis

Systems and methods for detecting data exfiltration using domain name system (DNS) queries include, in various embodiments, performing operations that include parsing a DNS query to determine whether that DNS query is likely to contain hidden data that is being exfiltrated from a system or network. Statistical methods can be used to analyze the DNS query to determine a likelihood whether each of a plurality of segments of the DNS query are indicative of data exfiltration methods. If one or multiple DNS queries are deemed suspicious based on the analysis, a security action on the DNS query can be performed, including sending an alert and/or blocking the DNS query from being forwarded.

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Tel Aviv, Israel

Liron Ben Kimon