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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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
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Abstract	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.
AbstractList	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.
Author	Sikarev, I. A. Tatarnikova, T. M.
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Cites_doi	10.1007/978-3-030-64430-7_1 10.1109/WCNC.2017.7925567 10.3390/sym13122425 10.3103/s0146411617080132 10.31799/1684-8853-2021-6-42-52 10.1007/978-3-030-93872-7_29 10.24411/2520-6990-2019-11088 10.25559/SITITO.14.201803.626-632
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Copyright	Allerton Press, Inc. 2024 ISSN 0146-4116, Automatic Control and Computer Sciences, 2024, Vol. 58, No. 8, pp. 1218–1225. © Allerton Press, Inc., 2024.Russian Text © The Author(s), 2023, published in Problemy Informatsionnoi Bezopasnosti, Komp’yuternye Sistemy, 2023, No. 4(57), pp. 84–94. Allerton Press, Inc. 2024.
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