/transactions/software/pagemill-advised-argyle-data-on-its-acquisition-by-mavenir /Assets/PMIB/transactions-images/2018/argyle.png argyle.png Argyle has been acquired by /Assets/PMIB/transactions-images/2018/mavenir.png mavenir.png Mavenir 142098

Pagemill Advised Argyle Data on its Acquisition by Mavenir

Richardson, Texas – February 5, 2018

Big Data Analytics and Advanced Machine Learning Platform provides Enhanced Insights and Protection for Mobile Devices and IoT

Mavenir, focused on transforming mobile network economics for Communications Service Providers (CSPs), today announced it has acquired Argyle Data, the first big data and machine learning security platform that delivers real-time anomaly detection and predictive analytics for mobile service providers and IoT networks. Engineered for the most challenging mobile environments, its supervised and unsupervised Machine Learning algorithms enable superior network traffic insights and comprehensive fraud detection on mobile voice and data planes.

Machine learning analytics is increasingly viewed as a key tool to improve the efficiency and profitability of mobile carrier networks1. With the introduction of 5G technology and the growth in new communication patterns, applications and devices, new vulnerabilities are being exposed.

As more and more IoT devices are connected, the threat vector will widen, placing more devices at risk of hacking, and more users at risk of having their personal data accessed, or falling victim to fraud2. The number of connected Internet of Things (IoT) devices worldwide will jump from nearly 27 billion in 2017 to 125 billion in 20303.

Rules-based and batch processing systems are insufficient. CSPs now require a modern, scalable approach to fight fraud, negative margins and arbitrage activity in real-time with supervised and unsupervised machine learning. An adaptive big data analytics approach is ideally suited to manage the volume, velocity and variety of structured and unstructured data over wireless networks.

Argyle Data technology can analyze terabytes of various data streams and protocols per second in real time with machine learning analytics that enable operators to reduce fraud and offer differentiated services because of their data insights.

“Unlike mobile devices like phones and tablets, IoT have little or no built in protection as security has not been a top priority in development, yet they are connected to mobile networks,” said Pardeep Kohli, President and CEO of Mavenir. “The addition of the Argyle team and its platform enhances Mavenir’s existing 5G, security and signaling machine learning suite to offer next generation revenue protection for mobile network operators and their subscribers.”

Added Kohli, “Only machine learning algorithms built to enable artificial intelligence systems are capable with these growing numbers of devices, to detect zero-day vulnerabilities in mobile networks, preventing increased financial risks.”

Mavenir’s cloud-native Security Suite includes Messaging Spam and Fraud control, Equipment Identity Register (EIR), Signaling Firewall, Session Border Controller (vSBC) and Mobile Edge Gateway enabling operators to understand, monitor, enforce and maintain network security. The security suite fully covers protection of the core mobile network including all communication services enabled by modern CSPs.

About Mavenir:

Mavenir is purpose-built to redefine mobile network economics for Communication Service Providers (CSPs). Our solutions pave the way to 5G with 100% software-based, end-to-end, Cloud Native network solutions. Leveraging industry-leading firsts in VoLTE, VoWiFi, Advanced Messaging (RCS), Multi-ID, vEPC and Cloud RAN, Mavenir accelerates network transformation for more than 250+ CSP customers in over 130 countries, serving over 50% of the world’s subscribers.
We embrace disruptive, innovative technology architectures and business models that drive service agility, flexibility, and velocity. With solutions that propel NFV evolution to achieve web-scale economics, Mavenir offers solutions to CSPs for revenue generation, cost reduction and revenue protection.