60504

Автор(ы): 

Автор(ов): 

3

Параметры публикации

Тип публикации: 

Доклад

Название: 

Selecting Architecture and Parameters of Deep Neural Networks for Computer Attack Classification

ISBN/ISSN: 

978-172816951-4

DOI: 

10.1109/FarEastCon50210.2020.9271458

Наименование конференции: 

  • International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon (Vladivostok, 2020)

Наименование источника: 

  • Proceedings of the International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon (Vladivostok, 2020)

Город: 

  • Vladivostok

Издательство: 

  • IEEE

Год издания: 

2020

Страницы: 

https://ieeexplore.ieee.org/document/9271458
Аннотация
The article states a problem of a multiclass network classification of computer attacks. To solve it, the authors consider an option of applying deep neural networks. For this research, the authors selected the architecture of a deep neural network based on the strategy combining a set of convolution and recurrent LSTM layers. The research suggests optimizing the neural network parameters on the basis of a genetic algorithm. The authors present the simulation results that show an opportunity to solve the task of network classification in real time.

Библиографическая ссылка: 

Амосов О.С., Амосова С.Г., Магола Д.С. Selecting Architecture and Parameters of Deep Neural Networks for Computer Attack Classification / Proceedings of the International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon (Vladivostok, 2020). Vladivostok: IEEE, 2020. С. https://ieeexplore.ieee.org/document/9271458.