Abstract: In one implementation, a method is disclosed comprising: determining, by a process, a network topology of a particular computer network and capabilities of particular devices within the network topology; determining, by the process, a logical framework of the particular computer network; mapping, by the process, access control and segmentation features of the particular devices to the logical framework based on the capabilities of the particular devices; and causing, by the process, mapped access control and segmentation features to be implemented to enforce the logical framework within the network topology.

Abstract: In one embodiment, a process discovers network topology information of a particular computer network and creates a plurality of zones of devices in the particular computer network based on the network topology information. The process also discovers network communication activity patterns and endpoints of the particular computer network and creates a plurality of conduits between devices of the particular computer network based on the network communication activity patterns and endpoints of the particular computer network and association of the devices within the plurality of zones as described above.

Abstract: In one embodiment, a device in a network receives a machine learning encoder and decoder trained by a supervisory service. The service trains the encoder and decoder using vibration measurement data sent to the service by a plurality of devices. The device trains, based on the received encoder, a classifier to determine whether vibration measurement data is indicative of a behavioral anomaly. The device receives vibration measurement data captured by a particular set of one or more vibration sensors of a monitored system. The device evaluates, using the trained decoder, the received vibration measurement data to determine whether the data is indicative of a structural anomaly in the monitored system. The device evaluates, using the trained classifier, the received vibration measurement data to determine whether the data is indicative of a behavioral anomaly in the monitored system.

Abstract: In one embodiment, a device in a network receives a machine learning encoder and decoder trained by a supervisory service. The service trains the encoder and decoder using vibration measurement data sent to the service by a plurality of devices. The device trains, based on the received encoder, a classifier to determine whether vibration measurement data is indicative of a behavioral anomaly. The device receives vibration measurement data captured by a particular set of one or more vibration sensors of a monitored system. The device evaluates, using the trained decoder, the received vibration measurement data to determine whether the data is indicative of a structural anomaly in the monitored system. The device evaluates, using the trained classifier, the received vibration measurement data to determine whether the data is indicative of a behavioral anomaly in the monitored system.