Palo Alto Networks, Inc.

Manish Mradul

William Redington Hewlett, Ii

Sujit Rokka Chhetri

Nandini Ramanan

Shan Huang

A multi-perspective user and entity behavior analytics (UEBA) system ('system') builds and maintains interchangeable modules for predicting likelihoods of anomalous user behavior at the scope of an actor (i.e., a user or entity) of an organization within time periods. Each module comprises probability models and/or machine learning models as sub-modules that model actor behavior at various levels of granularity with respect to usage of Software-as-a-Service applications. The system generates anomalousness scores by decorrelating likelihoods output by each sub-module and uses the anomalousness scores to monitor and perform corrective action based on anomalous actor behavior to maintain security posture across the organization.

Multi-perspective user and entity behavior analytics for software-as-a-service applications

Automated attribute scraping for security feature implementation with a single trained machine model across security features improves prediction quality and efficiency of predictions. A security feature implementation prediction system (system) generates search engine queries for each security feature based on high importance tokens for the security feature. The system ranks URLs returned from each search engine query for relevance, then preprocess and inputs content for top-ranked URLs into the trained machine learning models. The system identifies implemented security features output based on confidence values output by the trained machine learning model and identifies sentences that describe the implementations in corresponding content for top-ranked URLs.

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Milpitas, CA, United States of America

Manish Mradul