SAN MATEO, CA--(Marketwired - September 20, 2016) - Argyle Data, the leader in big data/machine learning analytics for mobile providers, and Carnegie Mellon University Silicon Valley's Department of Electrical and Computer Engineering today announced a new research paper, "Real-time Anomaly Detection in Streaming Cellular Network Data". The paper will be submitted for presentation at academic conferences during the first half of 2017.

Fraudulent usage of cellular networks is a growing threat for both network subscribers and operators that costs the industry an estimated $38 billion a year (CFCA Survey, 2015). Other emerging consumer behaviors including over the top (OTT) applications present a growing challenge to operator revenues. This has created an increasingly urgent need for robust analytics and detection solutions capable of identifying anomalous behavior and adapting to evolving network usage patterns in real-time.

"The sub-field of machine learning known as anomaly detection offers many attractive attributes for providing such solutions," said the paper's senior author Dr. Ole J. Mengshoel, Associate Research Professor in the Dept. of Electrical and Computer Engineering and Director, Intelligent and High-Performing Systems Lab at Carnegie Mellon University Silicon Valley.

"This approach described in this paper is unique," said Padraig Stapleton, VP of engineering at Argyle Data. "It describes a totally new machine learning method that includes significant developments to create a lightweight product that is fast to train and offers state of the art accuracy as well as other features to help analysts make rapid decisions -- all of which are essential for operators' production environments. The machine learning strategy is proving exponentially more effective in fighting fraud, delivering 350% better results than rules-based systems, and allowing analysts to shut down attacks in instants rather than hours or days."

Approaches currently used by mobile communications providers to detect fraud typically rely on static rules with pre-set thresholds. Moreover, such solutions cannot address issues on the data plane. However, since more and more fraud will occur on the data network in future, gaining visibility into the characteristics of data usage will be paramount. Due to the vast amount of data flowing across telecoms networks, big data analytics capabilities and the ability to analyze these using advanced machine learning are essential.

In this work, Dr. Mengshoel and first author David Staub, Data Scientist at Argyle Data, propose and validate a supervised and unsupervised machine learning-based approach that automatically learns the difference between normal and anomalous call patterns based on usage data.

Co-authors of the paper are Aniruddha Basak, PhD candidate, CMU, and machine learning expert Arshak Navruzyan.

To receive an executive preview of the research paper please contact

About Carnegie Mellon University Silicon Valley
The Carnegie Mellon Silicon Valley (CMU-SV) campus opened its doors in 2002, and has since grown into a vital connection between CMU's Pittsburgh campus and Silicon Valley, while expanding the university's degree offerings to include the Masters of Software Management, and three bi-coastal Information Technology Master's programs. From cyber physical systems research, to privacy, data analytics, connected vehicles and more, the CMU-SV campus brings the innovative spirit of Carnegie Mellon students to the start-ups of Silicon Valley.

About Argyle Data
Argyle Data is used by the world's leading mobile operators to detect mobile fraud and OTT threats that cost the industry $38 billion per year. Argyle Data's industry-leading native Hadoop application suite uses the latest machine learning technologies against a unique, comprehensive data lake to give communications service providers a 360-degree view of user activities, allowing them to detect in real time the previously undiscoverable revenue threats and attack patterns being waged against their networks. To learn more please visit:

Argyle Data Website
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