With the rapid development of the Industrial Internet of Things (IIoT), ensuring the security and privacy of network traffic has become particularly important. Classifying and identifying encrypted traffic is a critical step in enhancing network security, but traditional traffic classification methods often struggle to handle the complexities of the IIoT environment. In this paper, we propose the BCBA model, a bidirectional encoder representation from transformers (BERT)-based serial network model, which significantly improves the encrypted traffic classification performance and can be trained more than four times faster than the original BERT classifier. The BCBA method obtains word vector representations from the embedding layer of the pretrained BERT model, uses convolutional neural networks (CNNs) to extract local features, employs bidirectional long short-term memory (BiLSTM) networks to capture temporal dependencies in traffic data, and leverages a multihead self-attention mechanism to improve global dependency understanding. In experiments on six types of regular encrypted traffic from the ISCXVPN2016 dataset, the BCBA model achieved an F1 score of 99.10%, outperforming traditional traffic classification techniques across multiple performance metrics. This study demonstrates the effectiveness of deep learning in enhancing the security of the IIoT. It also provides new perspectives and technical routes for future research, particularly in encrypted traffic processing and classification applications.

中文翻译：

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BCBA：基于串行网络模型的 IIoT 加密流量分类器


随着工业物联网 （IIoT） 的快速发展，确保网络流量的安全性和隐私性变得尤为重要。对加密流量进行分类和识别是增强网络安全的关键步骤，但传统的流量分类方法通常难以处理 IIoT 环境的复杂性。在本文中，我们提出了 BCBA 模型，这是一种基于 transformers （BERT） 的双向编码器表示串行网络模型，它显著提高了加密流量分类性能，并且可以比原始 BERT 分类器快四倍以上进行训练。BCBA 方法从预训练 BERT 模型的嵌入层获取词向量表示，使用卷积神经网络 （CNN） 提取局部特征，采用双向长短期记忆 （BiLSTM） 网络来捕获交通数据中的时间依赖关系，并利用多头自注意力机制来提高全局依赖关系理解。在对 ISCXVPN2016 数据集中的六种常规加密流量进行的实验中，BCBA 模型获得了 99.10% 的 F1 分数，在多个性能指标上都优于传统的流量分类技术。这项研究证明了深度学习在增强 IIoT 安全性方面的有效性。它还为未来的研究提供了新的视角和技术路线，特别是在加密流量处理和分类应用方面。