Title: MultiNeigh-GNN: a multi-order neighbouring data fusion graph neural network for IoT intrusion detection

Authors: Zhihao Yin

Addresses: School of Information Engineering, Xuzhou College of Industrial Technology, Xuzhou, 221140, China

Abstract: Contemporary internet of things (IoT) intrusion detection techniques encounter difficulties in intricate networks: typically, they do not account for cross-hop dependencies outside immediate neighbours, and label sparsity obstructs the learning process from malicious traffic, hence hindering the detection of fresh attacks. We present a multi-neigh graph neural network (GNN) technique that integrates multi-order neighbouring data to overcome these challenges. This approach effectively captures multi-hop dependences and cross-hop attack propagation paths by integrating 1st, 2nd, and 3rd order neighbouring data via a multi-order neighbouring data fusion module, thereby substantially enhancing the model's capacity to recognise intricate attack patterns. The developed cross-neighbouring graph mutual-exclusion learning module efficiently identifies distinctive characteristics from sparse harmful traffic samples. Experimental findings on several publicly accessible IoT intrusion detection datasets demonstrate that MultiNeigh-GNN substantially surpasses current benchmark approaches, particularly in addressing cases characterised by intricate attack patterns and sparse harmful traffic.

Keywords: cross-neighbouring graph mutual-exclusion learning; graph neural networks; GNNs; IoT intrusion detection; multi-hop dependences; multi-order neighbouring.

DOI: 10.1504/IJSNET.2025.149131

International Journal of Sensor Networks, 2025 Vol.49 No.2, pp.111 - 122

Received: 13 Jul 2025
Accepted: 19 Aug 2025
Published online: 14 Oct 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article