US 12,348,525 B2
Generating zero-trust policy for application access using machine learning
Dianhuan Lin, Sunnyvale, CA (US); Raimi Shah, Austin, TX (US); Rex Shang, Los Altos, CA (US); Loc Bui, San Jose, CA (US); Subramanian Srinivasan, Milpitas, CA (US); William Fehring, Sunnyvale, CA (US); Arvind Nadendla, San Jose, CA (US); John A. Chanak, Saratoga, CA (US); Shudong Zhou, Fremont, CA (US); and Howie Xu, Palo Alto, CA (US)
Assigned to Zscaler, Inc., San Jose, CA (US)
Filed by Zscaler, Inc., San Jose, CA (US)
Filed on Oct. 13, 2021, as Appl. No. 17/499,942.
Prior Publication US 2023/0115982 A1, Apr. 13, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. H04L 9/40 (2022.01); G06N 5/022 (2023.01)
CPC H04L 63/104 (2013.01) [G06N 5/022 (2013.01); H04L 63/108 (2013.01); H04L 63/20 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A non-transitory computer-readable storage medium having computer readable code stored thereon for programming at least one processor to perform steps of:
obtaining log data for a plurality of users of an enterprise where the log data relates to usage of a plurality of applications by the plurality of users;
determining and defining, based on the obtained log data, i) one or more app-segments, each of the one or more app-segments comprising groupings of applications of the plurality of applications and ii) user-groups that are groupings of users of the plurality of users; and
providing access policy of the plurality of applications based on the defined user-groups and the one or more defined app-segments;
wherein the log data is transformed to feature vectors, and wherein the determining includes clustering with the feature vectors adapted to form any of an access matrix, app-segments, and user-groups, the clustering is based on a compressed feature vector, wherein the compressed feature vector defines a user app usage access pattern in a numerical format and is one of k-means clustering, DBScan, and Hierarchical DBScan.