Verizon Patent and Licensing Inc.

Mci Communications Corporation

Mci, Inc.

Verizon Business Global LLC

Worldcom, Inc.

Charles Alan Dallas

Saralyn M Mahone

James J Wagner

John Charles Gavan

Kevin Paul

Hans Van Arkel

Cheryl Herrington

Jim Richards

Terrill J Curtis

Mohammad Reza Tayebnejad

John Hans Van Arkel

Katrina Marie Kutchko

Yonatan Fridman

Allison J Ashford

Lori K Bogren

Karl Aric Van Camp

Paul Andrew Donfried

Vidhyaprakash Ramachandran

Terril J Curtis

Ken Mckeever

Terrell J Curtis

Systems and methods described herein provide a cyber risk assessment service. A computing device determines weights for techniques of a cyber security framework based on historical industry impact. The computing device associates an enterprise network with an industry identifier, obtains customer risk data for the enterprise network, and normalizes and/or combines the customer risk data to form normalized risk scores. The computing device maps the customer risk data to corresponding techniques in the cyber security framework, generates technique scores based on the mapping and the normalized risk scores, and generates weighted technique scores using some of the weights selected based on the industry identifier. The computing device calculates an overall security score for the enterprise network based on the weighted technique scores, identifies a corrective recommendation for the overall security score, and provides the overall security score and the corrective recommendation for presentation to a user.

Method and system for evaluating cyber security risks

A system, method and computer program product for processing event records. The present invention includes a detection layer, an analysis layer, an expert systems layer and a presentation layer. The layered system includes a core infrastructure and a configurable, domain-specific implementation. The detection layer employs one or more detection engines, such as, for example, a rules-based thresholding engine and a profiling engine. The detection layer can include an AI-based pattern recognition engine for analyzing data records, for detecting new and interesting patterns and for updating the detection engines to insure that the detection engines can detect the new patterns. In one embodiment, the present invention is implemented as a telecommunications fraud detection system. When fraud is detected, the detection layer generates alarms which are sent to the analysis layer. The analysis layer filters and consolidates the alarms to generate fraud cases. The analysis layer preferably generates a probability of fraud for each fraud case. The expert systems layer receives fraud cases and automatically initiates actions for certain fraud cases. The presentation layer also receives fraud cases for presentation to human analysts. The presentation layer permits the human analysts to initiate additional actions.

System and method for detecting and managing fraud

A computer is configured to receive an authentication request that identifies one or more authentication form factors, and for each form factor identified, further identifies at least one parameter. The computer is further configured to generate a risk score for the authentication request using the parameter, the risk score being based at least in part on a complexity associated with each of the one or more authentication form factors. The computer is further configured to provide the risk score to a requester.

Authentication risk evaluation

A data processing system program to develop, train, and implement a neural network for identifying customers who represent a bad debt risk is disclosed. A feature vector is applied to a neural network to generate outputs that approximate the relative likelihood that customers who are the subjects of the records used to generate the feature vector will be a bad debt risk. Statistical values relating categorical attributes of the customers to the likelihood of their becoming a bad debt risk are substituted for the categorical attributes, and the attributes are normalized before the feature vector is applied to the network. In one embodiment the customers are customers of a long distance service provider.

Artificial intelligence trending system

A system, method and computer program product for processing event records. The present invention includes a detection layer, an analysis layer, an expert systems layer and a presentation layer. The layered system includes a core infrastructure and a configurable, domain-specific implementation. The detection layer employs one or more detection engines, such as a rules-based thresholding engine and a profiling engine. The detection layer can include an Artificial Intelligence based pattern recognition engine for analyzing data records, for detecting new and interesting patterns and for updating the detection engines to insure that the detection engines can detect the new patterns. In one embodiment, the present invention is implemented as a telecommunications fraud detection system. When fraud is detected, the detection layer generates alarms which are sent to the analysis layer. The analysis layer filters and consolidates the alarms to generate fraud cases. The analysis layer preferably generates a probability of fraud for each fraud case. The expert systems layer receives fraud cases and automatically initiates actions for certain fraud cases. The presentation layer also receives fraud cases for presentation to human analysts. The presentation layer permits the human analysts to initiate additional actions.

System, method and computer program product for processing event records

A configurable and scalable rules-based thresholding system, method and computer program product for processing event records includes a core infrastructure and a configurable domain-specific implementation. The core infrastructure is generically employed regardless of the actual type of network being monitored. The domain-specific implementation is provided with user specific data and rules. The core infrastructure includes an event record enhancer which enhances events with additional data and a threshold detector which determines whether an enhanced event record, alone or in light of prior event records, exceeds one or more thresholds. The enhancer can access external databases for additional information related to an event record. In one embodiment, the enhancer generates feature vectors to represent enhanced event records. The threshold detector receives enhanced event records from the event record enhancer. The threshold detector selects one or more threshold rules from a database of threshold rules for applying to the enhanced event records. Where enhanced event records are in the form of feature vectors containing features and feature values, the threshold detector selects one or more threshold rules based upon the features or feature values in the vector. Where the feature vector includes a threshold for a feature value, the threshold detector tests the feature values against the threshold. The threshold detector may access prior event records in order to apply one or more threshold rules.

System, method and computer program product for a dynamic rules-based threshold engine

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Colorado Springs, CO, United States of America

Charles Alan Dallas