An IoT Security Start-up in San Jose CA
CloudPost Networks enters the IoT Security sector focused on hospital IoT. With machine learning algorithms, CloudPost is able to build individual behaviour and communication profiles of smart devices ranging from security cameras and access points to MRI scanners and smart beds.
Deviations from normal device behaviours are flagged, egregious deviations are blocked and reported based on security policies.
All communications are monitored, tracked and recorded for extensive reporting.
One of the best ways to prove ROI is for the product to provide a top-level reporting system that executives and decision makers can, at a glance, gain insight and take action.
A total number of devices are displayed with a risk breakdown of monitored devices into risk categories. Further breakdowns of devices by alarms and categories are also displayed. In an IoT attack, devices go through 5 stages of attack severity. A report of devices in all five steps are displayed.
Each device is profiled and its communication patterns are studied through machine learning, A device specific profile is developed over time and individual profile segments are recorded and broken down. Over time, a complete profile is created. Devices deviating from their profile are flagged up to the user for examination.
The Profile Segments are displayed as a dynamic and animated spiral and can be traversed by a slide line or by Next and Back movements.
Groups of device types can naturally communicate with other groups, other intranet services and to the outside internet. Focusing on one group will display the natural communication trends involving all three categories.
The graph can be rotated to give the user a better view based on connections.
In order to deep dive into individual device communications, when devices are flagged, a timeline of communications segmented by risk and communication volume is displayed for further diagnostics.