As Operational Technology increasingly integrates with Information Technology, the need for Intrusion Detection Systems becomes more important. This paper explores an unsupervised approach to anomaly detection in network traffic using $β$-Variational Autoencoders on the NSL-KDD dataset. We investigate two methods: leveraging the latent space structure by measuring distances from test samples to the training data projections, and using the reconstruction error as a conventional anomaly detection metric. By comparing these approaches, we provide insights into their respective advantages and limitations in an unsupervised setting. Experimental results highlight the effectiveness of latent space exploitation for classification tasks.

翻译：随着运营技术与信息技术的日益融合，入侵检测系统的需求变得愈发重要。本文探索了一种基于NSL-KDD数据集、利用β-变分自编码器进行网络流量无监督异常检测的方法。我们研究了两种技术路径：通过测量测试样本与训练数据投影在潜在空间中的距离以利用其结构特征，以及采用重构误差作为传统异常检测指标。通过对比这两种方法，我们揭示了其在无监督场景下各自的优势与局限性。实验结果突显了利用潜在空间特征在分类任务中的有效性。