Image credit:security intelligence
Image credit:security intelligence
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Carnegie Mellon University Silicon Valley’s Department of Electrical and Computer Engineering and Argyle Data, the leader in big data/machine learning analytics for mobile provider have 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.