CPC H04L 63/1458 (2013.01) [G06N 3/08 (2013.01); H04L 63/1466 (2013.01)] | 12 Claims |
1. A method to detect and distinguish traffic in a network operating environment, comprising:
generating a neural network model by:
receiving a first set of data representing current transactions;
receiving a second set of data representing transactions previously verified as being associated with human traffic;
artificially labeling each transaction in the first set of data with a first score indicative of a bot whether the transaction is a bot or a human;
labeling each transaction in the second set of data with a second score indicative of a human;
training a neural network using the first and second sets of data and the first and second scores as follows:
for each of a set of training iterations beginning with a first iteration:
determining whether the neural network converges;
responsive to a determination that the neural network does not converge, pruning one or more transactions from the first set of data that, based on a given threshold, cannot be discriminated from transactions in the second set of data;
determining whether the first set of data has sufficient samples;
responsive to a determination that the first set of data has sufficient samples, initiating a next iteration;
upon a determination that the neural network converges, outputting the neural network model; and
using the neural network model to discriminate traffic in the network operating environment as being either bot or human.
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