BCBA: An IIoT encrypted traffic classifier based on a serial network model

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. [Display omitted] •Proposed an innovative serial network architecture.•Developed an advanced pre-trained model specifically designed for encrypted traffic analysis.•Significantly improved the speed and accuracy of encrypted traffic classification in IIoT environments.•Proven practical applicability for real-world scenarios through superior performance metrics.