ZingBox is a new securitysolution that can visualize IoT (IIoT) devices used in enterprises and their communication status. Machine learning technology automatically inventories, classifies, and understands the operation of IoT devices in the monitored network, enabling real-time abnormality detection and risk management.
ZingBox was established in 2014, at loT security venture from silicon valley, USA. And more than 1100 companies installed it worldwide. And also, secures a total of 11.2 million loT devices.
It has machine learning technology specialized in understanding the IoT environment and devices. As a result, device classification and regular operation for each device are automatically identified and learned, and identifying deviations from the original process detects anomalies.
ZingBox provides as virtualization software. There do not have to install agents on the device side, and it installs without placing a load on existing IoT devices.
And, It is possible to create and apply policies as desired, and it also has a function to block unauthorized communications in cooperation with third-party firewalls *
IoT devices in the monitored network inventories, and further, machine learning automatically classifies devices and grasps the regular operation of each device. After learning for each environment is complete, real-time abnormality detection and risk management are possible.
It makes it possible to create and apply policies as desired, and it also has a function to block unauthorized communications by cooperating with other companies’ firewalls. All operations performed in conjunction with Zingbox Cloud. There is no need to install agents on the device side. Therefore, it is possible to comprehensively visualize and manage the environment without imposing a load on existing IoT devices.
By scrutinizing all the network traffic, it is possible to visualize the list of devices from which manufacturers and what categories exist.
Provides significant security improvements without any impact on the existing environment
Using unique machine learning algorithms to learn the characteristics of each IoT device’s communication, it is possible to detect unusual communication patterns such as DoS attacks and signs of malware infection and notify them as security alerts.
It is possible to scan for vulnerabilities on monitored devices. Along with the case where an abnormality detected in the communication pattern, it is possible to analyze the current situation with a detailed risk score even for small IoT devices that are difficult to manage.
Collaborative solutions system integration with vulnerability management solutions and various solutions such as NAC, NGFW, and Soar is possible. Use device information and security events captured by Zingbox to improve security and compliance measures.
ZingBox Inspector installation requirements (equivalent to 500 monitored devices)
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