With the rapid rise of the Internet of Things (IoT), ensuring the security of IoT devices has become essential. One of the primary challenges in this field is that new types of attacks often have significantly fewer samples than more common attacks, leading to unbalanced datasets. Existing research on detecting intrusions in these unbalanced labeled datasets primarily employs Convolutional Neural Networks (CNNs) or conventional Machine Learning (ML) models, which result in incomplete detection, especially for new attacks. To handle these challenges, we suggest a new approach to IoT intrusion detection using Self-Supervised Learning (SSL) with a Markov Graph Convolutional Network (MarkovGCN). Graph learning excels at modeling complex relationships within data, while SSL mitigates the issue of limited labeled data for emerging attacks. Our approach leverages the inherent structure of IoT networks to pre-train a GCN, which is then fine-tuned for the intrusion detection task. The integration of Markov chains in GCN uncovers network structures and enriches node and edge features with contextual information. Experimental results demonstrate that our approach significantly improves detection accuracy and robustness compared to conventional supervised learning methods. Using the EdgeIIoT-set dataset, we attained an accuracy of 98.68\%, a precision of 98.18%, a recall of 98.35%, and an F1-Score of 98.40%.

翻译：随着物联网的快速发展，确保物联网设备的安全性变得至关重要。该领域的主要挑战之一在于，新型攻击的样本数量通常远少于常见攻击，导致数据集不平衡。现有针对此类不平衡标记数据集的入侵检测研究主要采用卷积神经网络或传统机器学习模型，这会导致检测不完整，尤其对于新型攻击。为应对这些挑战，我们提出一种利用自监督学习与马尔可夫图卷积网络进行物联网入侵检测的新方法。图学习擅长建模数据内部的复杂关系，而自监督学习则能缓解新兴攻击标记数据有限的问题。我们的方法利用物联网网络的固有结构对图卷积网络进行预训练，随后针对入侵检测任务进行微调。马尔可夫链与图卷积网络的融合能够揭示网络结构，并利用上下文信息丰富节点与边的特征。实验结果表明，与传统监督学习方法相比，我们的方法显著提升了检测准确率与鲁棒性。在EdgeIIoT-set数据集上，我们实现了98.68%的准确率、98.18%的精确率、98.35%的召回率以及98.40%的F1分数。