Wireless sensor networks (WSNs) are important building blocks of the communication infrastructure in smart cities, intelligent transportation systems, Industry, Energy, and Agriculture 4.0, the Internet of Things, and other areas quickly adopting the concepts of fog and edge computing. Their cybernetic security is a major issue and efficient methods to improve their safety and reliability are required. Intrusion detection systems (IDSs) are complex systems that discover cybernetic attacks, detect malicious network traffic, and, in general, protect computer systems. Artificial neural networks are used by a variety of advanced intrusion detection systems with outstanding results. Their successful use in the specific conditions of WSNs requires efficient learning, adaptation, and inference. In this work, the acceleration of a neural intrusion detection model, developed specifically for wireless sensor networks, is proposed and studied, especially from the learning and classification accuracy and energy consumption points of view.

中文翻译：

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无线传感器网络高效神经入侵检测的设计与分析

无线传感器网络 (WSN) 是智慧城市、智能交通系统、工业、能源和农业 4.0、物联网和其他领域快速采用雾和边缘计算概念的通信基础设施的重要构建块。它们的控制论安全是一个主要问题，需要有效的方法来提高它们的安全性和可靠性。入侵检测系统 (IDS) 是发现控制论攻击、检测恶意网络流量以及通常保护计算机系统的复杂系统。人工神经网络被各种先进的入侵检测系统使用，并取得了出色的效果。它们在 WSN 的特定条件下的成功使用需要有效的学习、适应和推理。在这项工作中，