Patternex, Inc.

Citrix Systems, Inc.

Vamsi Korrapati

Constantinos Bassias

Uday Veeramachaneni

Ignacio Arnaldo

Prakash Khemani

Rajiv Mirani

Abhishek Chauhan

Ke Li

Anoop Reddy

Puneet Agarwal

Srinivasan Thirunarayanan

Methods and apparatuses employing outlier score detection method and apparatus for identifying and detecting threats to an enterprise or e-commerce system are disclosed, including grouping log lines belonging to one or more log line parameters from one or more enterprise or e-commerce system data sources and/or from incoming data traffic to the enterprise or e-commerce system; extracting one or more features from the grouped log lines into one or more features tables; using one or more statistical models on the one or more features tables to identify statistical outliers; using the one or more features tables to create one or more rules for identifying threats to the enterprise or e-commerce system; and using the one or more rules on incoming enterprise or e-commerce system data traffic to detect threats to the enterprise or e-commerce system. Other embodiments are described and claimed.

Computer-implemented process and system employing outlier score detection for identifying and detecting scenario-specific data elements from a dynamic data source

Methods and apparatuses employing copula optimization in building multivariate statistical models for identifying and detecting threats to an enterprise or e-commerce system are disclosed, including grouping log lines belonging to one or more log line parameters from one or more enterprise or e-commerce system data sources and/or from incoming data traffic to the enterprise or e-commerce system; extracting one or more features from the grouped log lines into one or more features tables; using one or more statistical models on the one or more features tables to identify statistical outliers and using the one or more rules on incoming enterprise or e-commerce system data traffic to detect threats to the enterprise or e-commerce system. Other embodiments are described and claimed.

Copula optimization method and apparatus for identifying and detecting threats to an enterprise or e-commerce system and other applications

Disclosed herein are a method and system for training a big data machine to defend, retrieve log lines belonging to log line parameters of a system's data source and from incoming data traffic, compute features from the log lines, apply an adaptive rules model with identified threat labels produce a features matrix, identify statistical outliers from execution of statistical outlier detection methods, and may generate an outlier scores matrix. Embodiments may combine a top scores model and a probability model to create a single top scores vector. The single top scores vector and the adaptive rules model may be displayed on a GUI for labeling of malicious or non-malicious scores. Labeled output may be transformed into a labeled features matrix to create a supervised learning module for detecting new threats in real time and reducing the time elapsed between threat detection of the enterprise or e-commerce system.

Method and system for training a big data machine to defend

Methods and apparatuses for identifying and detecting threats to an enterprise or e-commerce system are disclosed, including grouping log lines belonging to one or more log line parameters from one or more enterprise or e-commerce system data sources and/or from incoming data traffic to the enterprise or e-commerce system; extracting one or more features from the grouped log lines into one or more features tables; using one or more statistical models on the one or more features tables to identify statistical outliers; labeling the statistical outliers to create one or more labeled features tables; using the one or more labeled features tables to create one or more rules for identifying threats to the enterprise or e-commerce system; and using the one or more rules on incoming enterprise or e-commerce system data traffic to detect threats to the enterprise or e-commerce system. Other embodiments are described and claimed.

Method and apparatus for identifying and detecting threats to an enterprise or e-commerce system

The present disclosure provides solutions for an enterprise providing services to a variety of clients to enable the client to use the resources provided by the enterprise by modifying URLs received and the URLs from the responses from the servers to the client's requests before forwarding the requests and the responses to the intended destinations. An intermediary may identify an access profile for a clients' request to access a server via a clientless SSL VPN session. The intermediary may detect one or more URLs in content served by the server in response to the request using one or more regular expressions of the access profile. The intermediary may rewrite or modify, responsive to detecting, the one or more detected URLs in accordance with a URL transformation specified by one or more rewrite policies of the access profile. The response with modified URLs may be forwarded to the client.

Systems and methods for configuration driven rewrite of SSL VPN clientless sessions

Systems and methods for configuring and evaluating policies that direct processing of one or more data streams are described. A configuration interface is described for allowing users to specify object oriented policies. These object oriented policies may allow any data structures to be applied with respect to a payload of a received packet stream, including any portions of HTTP traffic. A configuration interface may also allow the user to control the order in which policies and policy groups are executed, in addition to specifying actions to be taken if one or more policies are undefined. Systems and methods for processing the policies may allow efficient processing of object-oriented policies by applying potentially complex data structures to unstructured data streams. A device may also interpret and process a number of flow control commands and policy group invocation statements to determine an order of execution among a number of policies and policy groups. These policy configurations and processing may allow configuration and processing of complex network behaviors relating to load balancing, VPNs, SSL offloading, content switching, application security, acceleration, and caching.

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San Jose, CA, United States of America

Vamsi Korrapati