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...

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Bibliographic Details
Published in:Automatic control and computer sciences Vol. 58; no. 8; pp. 1218 - 1225
Main Authors: Sikarev, I. A., Tatarnikova, T. M.
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01.12.2024
Springer Nature B.V
Subjects:
Algorithms
Artificial neural networks
Computer Science
Control Structures and Microprogramming
Datasets
Neural networks
Software
Special libraries
artificial neural network
loss function
intrusion detection system
training
classification
dataset
architecture optimization
ISSN:0146-4116, 1558-108X
Online Access:Get full text
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Summary: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.
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ISSN:0146-4116
1558-108X
DOI:10.3103/S0146411624700858