ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2022-22-2-317-323ntv-164Research ArticleКОМПЬЮТЕРНЫЕ СИСТЕМЫ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИCOMPUTER SCIENCEНовая структура маршрутизации для предотвращения черных дыр в беспроводных датчиках с использованием гибридной сверточной сетиA novel framework for the prevention of black-hole in wireless sensors using hybrid convolution networkhttps://orcid.org/0000-0001-5093-5822КолангиаппанД.KolangiappanJ.

Колангиаппан Джаяраман — PhD, исследователь 

 Салем, 636011 

Jayaraman Kolangiappan — PhD, Research Scholar 

Salem, 636011

jkakshiya@gmail.cohttps://orcid.org/0000-0001-5131-7428Сентил КумарА.Senthil KumarA.

Сентил Кумар Ангамуту — PhD, доцент 

Намаккал, 637002 

Angamuthu Senthil Kumar — PhD, Assistant Professor 

 Nxamakkal, 637002 

senthilkumarmca76@gmail.comУниверситет ПериярИндияPeriyar UniversityIndiaГосударственный художественный колледж Ариньяра АнныИндияArignar Anna Government Arts CollegeIndia202214122024222317323Copyright © Колангиаппан Д., Сентил Кумар А., 20242024Колангиаппан Д., Сентил Кумар А.Kolangiappan J., Senthil Kumar A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/164

С увеличением количества устройств в беспроводных сенсорных сетях (WSN) появились дополнительные проблемы. В связи с этим возрастают требования к защите таких сетей от внешних воздействий. Проблемы безопасности WSN определяются путем решения задачи оптимальной маршрутизации пути, энергосбережения и др. Предложена гибридная модель эффективной системы маршрутизации и доставки пакетов для предотвращения атак черных дыр. Такой тип атаки считается наиболее распространенным в сети благодаря своим уникальным характеристикам. Для их обнаружения предложена модель глубокого обучения с использованием сверточной нейронной сети (CNN). Алгоритм обучения должен быть надежным и заслуживающим доверия, чтобы анализ атак можно было рассматривать на разных уровнях для изучения их интеллектуального поведения. Рассмотрена задача поиска оптимального кратчайшего пути с использованием Deep Q-Learning и CNN для выполнения эффективной маршрутизации и доставки пакетов более безопасным способом. В результате моделирования достигнута точность 98,57 %.

Problems of Wireless Sensor Networks (WSN) are associated with a significant increase in the number of devices on these networks. In this regard, the requirements for the protection and the security of WSN from external influences are increasing significantly. WSN security problems are solved by solving the problem of optimal path routing, energy conservation, and so on. This paper proposes a hybrid model of an efficient packet routing and delivery system to prevent Black-hole attacks. This type of attack is considered the most common on the network due to its unique characteristics. To detect such attacks, a deep learning model using a Convolutional Neural Network (CNN) is proposed. The learning algorithm must be reliable and trustworthy so that attack analysis can be considered at different levels to study the intelligent behavior of network attacks. The paper considers the problem of finding the optimal shortest path using Deep Q-Learning and convolutional neural networks to perform efficient routing and delivery of packets in a safer way. As a result of simulation, the achieved accuracy reached 98.57 %.

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The authors declare that there are no conflicts of interest present.