Abstract: Systems, devices and techniques for an adaptive application-specific probing scheme are disclosed. An example network device includes memory configured to store a network address and probe protocol usable for probing a first network device associated with a source of an application, and one or more processors configured to determine a network address and probe protocol usable for probing the first network device, wherein the first network device comprises a server that is responsive to the probing, the server executing the application for the data flow, or a closest network device, to the server, that is responsive to the probing. The one or more processors are also configured to send to a second network device at a location serviced by the application, a message specifying the network address and probe protocol usable for probing the first network device.

Abstract: Modulated radio frequency (RF) packets are received from a wireless device, and converted to modulated baseband packets. Baseband parameters are derived from the modulated baseband packets. A baseband profile including the baseband parameters is created for the wireless device. A database including baseband profiles of wireless devices is accessed. The baseband profiles are classified under known device types based on baseband parameters included in the baseband profiles. The baseband parameters of the baseband profile are compared to corresponding baseband parameters of the baseband profiles in the database. If the comparing indicates a match between the baseband profile and one of the baseband profiles, the wireless device is classified under the known device type of the one of the baseband profiles, and the baseband profile is stored in the database under the known device type.

Abstract: A computing device determines an onboarding algorithm to use for onboarding a wireless device. The computing device determines, based on the onboarding algorithm, a first set of predefined information and a second set of dynamically generated information to use as inputs to the onboarding algorithm. The computing device generates, via the onboarding algorithm, a set of credentials based on the first set of predefined information and the second set of dynamically generated information, and uses the set of credentials to secure a connection for onboarding the wireless device.

Abstract: In one embodiment, a device obtains radio frequency (RF) characteristic data for a wireless client. The device inputs the RF characteristic data for the wireless client to a deep learning-based encoder. The device learns a latent space representation of the RF characteristic data from the encoder. The device uses the learned latent space representation as a unique signature to identify the wireless client in a wireless network.

Abstract: Modulated radio frequency (RF) packets are received from a wireless device, and converted to modulated baseband packets. Baseband parameters are derived from the modulated baseband packets. A baseband profile including the baseband parameters is created for the wireless device. A database including baseband profiles of wireless devices is accessed. The baseband profiles are classified under known device types based on baseband parameters included in the baseband profiles. The baseband parameters of the baseband profile are compared to corresponding baseband parameters of the baseband profiles in the database. If the comparing indicates a match between the baseband profile and one of the baseband profiles, the wireless device is classified under the known device type of the one of the baseband profiles, and the baseband profile is stored in the database under the known device type.