Detecting Attacks Using Artificial Neural Networks

The developed neural network attack detection algorithm, whose peculiarity lies in the possibility of launching two parallel processes, is described: searching for the optimal model of an artificial neural network and normalization of the training sample data. It is shown that the artificial neural network architecture is selected taking into account the loss function for a limited set of attack classes. The application of TensorFlow and Keras Tuner libraries (frameworks) for the software implementation of an attack detection algorithm is shown. An experiment on the selection of neural network architecture and its training is described. The accuracy obtained in experiments is 94–98% for different classes of attacks.