Patternex, Inc.

Other

Packet Design, Inc.

Constantinos Bassias

Ignacio Arnaldo

Vamsi Korrapati

Uday Veeramachaneni

Ke Li

Cengiz Alaettinoglu

Hariharan Ananthakrishnan

Satish K Kanna

Siddharth Taneja

Victor Hau Chen

Ankit Arun

Roopesh Palasdeokar

Mei Lem

Amit Yajurvedi

Disclosed is a method and system for detecting malicious entities and malicious behavior in a time evolving network via a graph framework by modeling activity in a network graph representing associations between entities. The system utilizes classification methods to give score predictions indicative of a degree of suspected maliciousness, and presents a unified graph inference method for surfacing previously undetected malicious entities that utilizes both the structure and behavioral features to detect malicious entities.

Method and system for employing graph analysis for detecting malicious activity in time evolving networks

Identifying and detecting threats to an enterprise system groups log lines from enterprise data sources and/or from incoming data traffic. The process applies artificial intelligence processing to the statistical outlier in the event of the statistical outliers comprises a sparsely labelled real data set, by receiving the sparsely labelled real data set for identifying malicious data and comprising real labelled feature vectors and generating a synthetic data set comprising a plurality of synthetic feature vectors derived from the real, labelled feature vectors. The process further identifies the sparsely labelled real data set as a local data set and the synthetic data set as a global set. The process further applies a transfer learning framework for mixing the global data set with the local data set for increasing the precision recall area under curve (PR AUC) for reducing false positive indications occurring in analysis of the threats to the enterprise.

Method and system for generating synthetic feature vectors from real, labelled feature vectors in artificial intelligence training of a big data machine to defend

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

A system and method collects information for VPN traffic from non edge routers that are coupled to edge routers and identifies the path the traffic took and the customer corresponding to the VPN. The system and method also identifies the ingress router coupled to the non edge router from which the traffic was collected. The system and method may assign identifiers to route targets.

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San Jose, CA, United States of America

Constantinos Bassias