Intrusion Detection for Wireless Sensor Network Using Graph Neural Networks

Wireless Sensor Networks (WSNs) are rapidly employed in many applications due to highly demanded autonomous systems. These networks are of immense importance due to their ability to collect data from remote and challenging environments, their impact on various sectors like healthcare, agriculture, industry, environment, and their role in enabling smart technologies for a sustainable, secure, and connected future. Nevertheless, these systems can be attacked by adversaries. Usually, the WSNs are designed with lightweight sensor nodes with limited computation and memory resources. Therefore, employing a firewall system on every sensor node is unacceptable. This paper tackled this problem with a very lightweight Graph Neural Network-based model. The conducted experiment performed in this work demonstrates promising attack-type detection by our proposed approach to the WSN-DS dataset. In this article, our proposed method is compared with other the-state-of-the-art works, and we could discover all Blackhole attacks, one of the most common Denial-of-Service attacks.