Design and analysis of efficient neural intrusion detection for wireless sensor networks

Abstract

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.