Patternex, Inc.

Uday Veeramachaneni

Constantinos Bassias

Vamsi Korrapati

Ignacio Arnaldo

Ke Li

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.

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San Jose, CA, United States of America

Uday Veeramachaneni