LEVL Delivers Breakthrough Device Typing Capabilities to LEVL-IQ Platform

Enhanced Physical Layer Approach Delivers Greater Accuracy and Auto-Type Learning in Near Real-Time

LEVL Technologies, Inc. today announced the availability of their next-generation device context service. Device context, also known as device typing, is the ability to offer context on devices in the network such as the type of the devices, the manufacturer, model, and operating system. This is an important part of all device intelligence platforms that network operators use to secure the network, optimize user experience and offer better customer support.

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However, as OS makers have increasingly obfuscated network data, device intelligence has become much more challenging. As an example, the recent release of iOS15 has created significant challenges for network operators to gain clarity on the type of the devices in their network. The LEVL-IQ device intelligence platform now uses enhanced physical layer (Radio Frequency “RF”) device information, and machine learning, to offer unparalleled performance in detection of a device’s type, manufacturer, model, and OS version with a high level of accuracy and in near real-time. Additionally, the enhanced machine learning approach allows the system to learn new device types automatically.

“Technically, the addition of more physical layer information and enhancing the machine learning techniques allow us to more accurately determine what types of devices are on the network and to automatically learn new devices we have never seen,” said Michael Estrin, CTO and cofounder of LEVL. “This is really a technical breakthrough and, as best we know, it is a unique solution in the market. While other solutions rely only on network data or personal information, which has shown to be insufficient for accurate device intelligence, we also leverage many forms of physical layer data, i.e., features from the RF transmission of devices. This allows us to offer more insights in a near real-time manner and future-proofs our performance. In fact, as a simple example, and to the best our knowledge, our platform is the only one that can accurately differentiate between an iPhone, an Apple watch and an iPad. All other solutions label these as the same “iOS device.”

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