MOUNTAIN VIEW, Calif.--(BUSINESS WIRE)--ZingBox, leading a new generation of cybersecurity solutions focused on service protection, today unveiled IoT Guardian: the industry’s first offering that uses Deep Learning algorithms to discern each device’s unique personality and enforce acceptable behavior. IoT Guardian’s self-learning approach continually builds on previous knowledge to discover, detect and defend critical IoT services and data while avoiding false positives with 99.9 percent accuracy.
“Enterprises, healthcare organizations and manufacturing floors are embracing the digital age with a wide variety of connected devices to improve productivity, decision making and service delivery. But the resulting Internet of Things is highly vulnerable and lacks a crucial component: Trust,” said Xu Zou, co-founder and CEO, ZingBox. “ZingBox is first to provide a solution based on Deep Learning that recognizes each device’s personality to enable what customers demand: the Internet of Trusted Things.”
Traditional IT security relies on detecting malware on a few well-understood platforms. Focused primarily on data protection, such solutions are unable to defend the diverse set of IoT devices that sport a variety of non-standard or customized operating systems. To instill trust in diverse IoT assets, ZingBox invented a new fully non-disruptive approach that discerns each device’s personality, monitors all activities and enforces trusted behavior. The new device-personality approach was first conceptualized at Stanford University by ZingBox founders to address zero-day cyber and insider threats and eliminate the need for installing software agents on each device.
“Medical device networks in a hospital are not rigorously monitored. We needed a solution that would generate a real-time inventory of medical devices across the hospital network and evaluate the risk exposure,” said Jerry Marshall, Director Information Services and Telecommunications, United Regional Health Care System. “The ZingBox solution discovered over 95 percent of medical devices compared to current tools that could only detect about 5 percent. The intelligence and accuracy of elaborate device personalities allowed us to turn ZingBox into a tool that regulates medical device behaviors.”
Industry watchers project the number of connected devices to be over 20 billion by the year 2020. But that growth has fueled concerns about vulnerability and fears for loss of privacy, brand value, critical services, and even lives. Lack of visibility into each device’s activities has led to an overall sense of “digital mistrust.”
“From heart monitors to industrial robots, IoT is entering a new business-critical and even life-critical phase where millions of diverse but vulnerable devices deliver crucially important services,” said Peter ffoulkes, a partner and analyst at OrionX. “This new phase goes beyond the second generation of IoT to deliver a new class of cyber risk management solutions. Such new solutions are adaptive and must leverage, rather than fight, the volume and variety of devices and their unique personalities.”
ZingBox IoT Guardian is available now with a tiered SaaS pricing model based on number of devices being secured. For more information, visit www.zingbox.com.
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Enabling the Internet of Trusted Things, ZingBox provides hospitals, companies and manufacturing facilities with Internet of Things (IoT) security software that helps ensure service delivery. ZingBox’s new approach is based on Deep Learning and enforcement of trusted behavior. Founded by Silicon Valley veterans with expertise in cybersecurity, IoT, Deep Learning, and networking, ZingBox was selected by the Stanford StartX program, and was recently named one of NetworkWorld’s hottest security startups. For more information, visit www.zingbox.com.