HF-IDS: A Heuristic Factors based Semi-Supervised Model for Intrusion Detection Systems in IoT Networks

Lang He, Xiao-Ping Zhang, Konstantinos N. Plataniotis

Abstract: Semi-supervised learning in intrusion detection systems (IDS) faces three major challenges: the scarcity of labeled samples, class imbalance, and distribution divergence between labeled and unlabeled data. Moreover, the third challenge may lead to an extreme situation where labeled data fail to cover all categories. To address these issues, we develop a heuristic factors based semi-supervised model named HF-IDS. Specifically, we employ symbolic regression, a novel feature engineering technique, to generate class-indicating factors. These factors guide a multi-level clustering for pseudo-label addition to some unlabeled data. For classification, we construct an Ensemble of Graph Neural Networks (E-GNNs). Different from common ensemble learning methods, each of the GNN classifiers is equipped with a unique graph filter constructed by the factors. Through these filters, we topologically reweight different classes to enhance the model’s effectiveness in handling with class imbalance problem. The model is evaluated in both multi-class and binary classification settings, with the binary case simulating the extreme missing-category scenario. Experiments on NSL-KDD and CICIDS-2017 datasets show that HF-IDS consistently outperforms state-of-the-art baselines in accuracy, precision, recall, and F1_score.

External IDs:doi:10.1109/jiot.2026.3657655