CPC H04L 41/147 (2013.01) [G06N 3/042 (2023.01); G06N 3/044 (2023.01); G06N 3/088 (2013.01); H04L 41/16 (2013.01)] | 20 Claims |
1. A computer-implemented method, comprising:
for each device included in a plurality of interconnected devices within a device network, executing a trained recurrent neural network (RNN) that generates a set of predicted attribute values for a set of device attributes associated with the device based on input that includes at least one set of past attribute values for the set of device attributes, wherein the set of device attributes characterizes an operation of the device within the device network, and wherein the set of predicted attribute values is associated with a forward-looking time step and the at least one set of past attribute values is associated with one or more time steps occurring prior to the forward-looking time step; and
performing one or more classification operations based on the sets of predicted attribute values for the plurality of interconnected devices and one or more machine-learned classification criteria to predict a plurality of probabilities for a plurality of levels of degradation in a network availability of the device network, wherein at least one preemptive action is subsequently performed on the device network based on the plurality of probabilities for the plurality of levels of degradation in the network availability.
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