对比四种模型在物联网僵尸网络检测中的表现，发现图神经网络未必更优。
Are GNNs Worth the Effort for IoT Botnet Detection? A Comparative Study of VAE-GNN vs. ViT-MLP and VAE-MLP Approaches
- 用变分自编码器+MLP/GNN/ViT构建检测模型，比较其性能差异。
- 多分类任务中，基于GNN的模型准确率仅86%~89%，远低于其他模型。
- 在二分类任务中，所有模型均超99.9%，说明问题简单时模型差异不明显。
由于基于物联网的僵尸网络攻击呈指数级增长，研究人员探索了多种先进方法进行降维和攻击检测以提升物联网安全。其中，变分自编码器（VAE）、视觉变压器（ViT）以及图神经网络（GNN），包括图卷积网络（GCN）和图注意力网络（GAT），在攻击检测领域受到广泛关注。本研究评估了四种前沿深度学习架构在物联网僵尸网络检测中的有效性：基于VAE编码器的MLP、GCN、GAT，以及基于ViT编码器的MLP。实验在广泛使用的物联网基准数据集N-BaIoT上进行，涵盖二分类与多分类任务。在二分类任务中，所有模型准确率、召回率、精确率和F1分数均超过99.93%，性能无显著差异。而在多分类任务中，基于GNN的模型表现明显逊于VAE-MLP和ViT-MLP，准确率分别为86.42%（VAE-GCN）、89.46%（VAE-GAT）、99.72%（VAE-MLP）和98.38%（ViT-MLP）。
原文摘要 · Abstract (English)
Due to the exponential rise in IoT-based botnet attacks, researchers have explored various advanced techniques for both dimensionality reduction and attack detection to enhance IoT security. Among these, Variational Autoencoders (VAE), Vision Transformers (ViT), and Graph Neural Networks (GNN), including Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT), have garnered significant research attention in the domain of attack detection. This study evaluates the effectiveness of four state-of-the-art deep learning architectures for IoT botnet detection: a VAE encoder with a Multi-Layer Perceptron (MLP), a VAE encoder with a GCN, a VAE encoder with a GAT, and a ViT encoder with an MLP. The evaluation is conducted on a widely studied IoT benchmark dataset--the N-BaIoT dataset for both binary and multiclass tasks. For the binary classification task, all models achieved over 99.93% in accuracy, recall, precision, and F1-score, with no notable differences in performance. In contrast, for the multiclass classification task, GNN-based models showed significantly lower performance compared to VAE-MLP and ViT-MLP, with accuracies of 86.42%, 89.46%, 99.72%, and 98.38% for VAE-GCN, VAE-GAT, VAE-MLP, and ViT-MLP, respectively.
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