Netskope, Inc.

Juniper Networks, Inc.

Redline Networks, Inc.

Steve Malmskog

Krishna Narayanaswamy

Ravi Ithal

Arjun Sambamoorthy

Abhay Kulkarni

Ariel Faigon

Jeevan Tambuluri

Chetan Anand

Prashanth Arun

Israel L'Heureux

Shankaran Gnanashanmugam

Roger Allen Chickering

Khurram Saqlain

The technology disclosed relates to method and system of monitoring and controlling exfiltration of enterprise data stored on the cloud computing service (CCS). The method and system includes using a cross-application monitor to detect a could service application programming interface (API) in use and a function or activity being performed via the CCS API. The method and system determines the function or activity by parsing a data stream based on the CCS API and identifies a content of the enterprise data subject to content control by the application of a content inspection rule data subject to content control. The method and system selects a security action being applied to the enterprise data to prevent exfiltration based on the classification of the inspected data and policies applicable to the content subject to content control.

Method and system of monitoring and controlling exfiltration of enterprise data on cloud

The technology relates to machine responses to anomalies detected using machine learning based anomaly detection. In particular, to receiving evaluations of production events, prepared using activity models constructed on per-tenant and per-user basis using an online streaming machine learner that transforms an unsupervised learning problem into a supervised learning problem by fixing a target label and learning a regressor without a constant or intercept. Further, to responding to detected anomalies in near real-time streams of security-related events of tenants, the anomalies detected by transforming the events in categorized features and requiring a loss function analyzer to correlate, essentially through an origin, the categorized features with a target feature artificially labeled as a constant. An anomaly score received for a production event is determined based on calculated likelihood coefficients of categorized feature-value pairs and a prevalencist probability value of the production event comprising the coded features-value pairs.

Machine learning based anomaly detection initialization

A computer-implemented method is described to monitor and control enterprise information stored on a cloud computing service (CCS). The method includes using a cross-application monitor to detect a cloud computing service (CCS) application programming interface (API) in use and a function or an activity being performed via the CCS API. The method also includes determining the function or the activity being performed via the CCS API by parsing a data stream based on the CCS API and identifying content being transmitted to the CCS. The method further includes applying a content inspection rule to find strings and interrelated strings in the content that are subject to content control and triggering a security action responsive to finding the strings and interrelated strings subject to content control in the parsed stream.

Systems and methods of monitoring and controlling enterprise information stored on a cloud computing service (CCS)

Machine learning based anomaly detection and response

The technology disclosed relates to securely encrypting a document. In particular, it relates to accessing a key-manager with a triplet of organization identifier, application identifier and region identifier and in response receiving a triplet-key and a triplet-key identifier that uniquely identifies the triplet-key. Also, for a document that has a document identifier (ID), the technology disclosed relates to deriving a per-document key from a combination of the triplet-key, the document ID and a salt. Further, the per-document key is used to encrypt the document.

Systems and methods of cloud encryption

The technology disclosed relates to machine learning based anomaly detection. In particular, it relates to constructing activity models on per-tenant and per-user basis using an online streaming machine learner that transforms an unsupervised learning problem into a supervised learning problem by fixing a target label and learning a regressor without a constant or intercept. Further, it relates to detecting anomalies in near real-time streams of security-related events of one or more tenants by transforming the events in categorized features and requiring a loss function analyzer to correlate, essentially through an origin, the categorized features with a target feature artificially labeled as a constant. It further includes determining an anomaly score for a production event based on calculated likelihood coefficients of categorized feature-value pairs and a prevalencist probability value of the production event comprising the coded features-value pairs.

Anomaly detection with machine learning

Machine learning based anomaly detection

Systems and methods of per-document encryption of enterprise information stored on a cloud computing service (CCS)

A system includes a virtual machine (VM) server and a policy engine server. The VM server includes two or more guest operating systems and an agent. The agent is configured to collect information from the two or more guest operating systems. The policy engine server is configured to: receive the information from the agent; generate access control information for a first guest OS, of the two or more guest operating systems, based on the information; and configure an enforcer based on the access control information.

Security enforcement in virtualized systems

A networking system, device, and method are provided. The networking device typically includes a user-defined ruleset including HTTP request rules and HTTP response rules. The networking device may further include a request processor configured to receive an incoming HTTP request from the client, apply HTTP request rules to the incoming HTTP request, to thereby produce a modified HTTP request, and send the modified HTTP request to the server. The networking device may further include a response processor configured to receive an HTTP response to the modified HTTP request from the server, apply the HTTP response rules to the HTTP response, to thereby produce a modified HTTP response, and send the modified HTTP response to the client.

Rule-based networking device

A server load test system, device, and method for generating an artificial load and evaluating the performance of a web server. The load test system configured to generate a pool of synthetic clients and direct each synthetic client to request resources from the web server.

Growing community of inventors

Los Altos, CA, United States of America

Steve Malmskog