55642

Автор(ы): 

Автор(ов): 

3

Параметры публикации

Тип публикации: 

Доклад

Название: 

Recognition of Abnormal Traffic Using Deep Neural Networks and Fuzzy Logic

ISBN/ISSN: 

978-172810061-6

DOI: 

10.1109/FarEastCon.2019.8934327

Наименование конференции: 

  • 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon, Vladivostok, Russia)

Наименование источника: 

  • Proceedings of the 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon, Vladivostok, Russia)

Город: 

  • Владивосток

Издательство: 

  • Institute of Electrical and Electronics Engineers Inc.

Год издания: 

2019

Страницы: 

https://ieeexplore.ieee.org/document/8934327
Аннотация
Recognition of the abnormal traffic using the deep learning is proposed. The dual deep neural network architecture for detecting the DOS attacks based on a combination of convolutional layers is proposed. The new thing is the classification strengthening realized by the reinforcement of the input vector with its cluster evaluation. The software was implemented, the testing bench was assembled and a semi-natural experiment was conducted on the basis of the local computer network of the University. As a result of the tests, 87% accuracy of attack recognition was achieved.

Библиографическая ссылка: 

Амосов О.С., Иванов Ю.С., Амосова С.Г. Recognition of Abnormal Traffic Using Deep Neural Networks and Fuzzy Logic / Proceedings of the 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon, Vladivostok, Russia). Владивосток: Institute of Electrical and Electronics Engineers Inc., 2019. С. https://ieeexplore.ieee.org/document/8934327.