Efficient Malicious Traffic Classification Methods based on Semi-supervised Learning

Hu, Xiangming; Ning, Jinhui; Yin, Jie; Yang, Jie; Adebisi, Bamidele; Gacanin, Haris

doi:10.1109/dsa56465.2022.00039

Cited by 2 publications

(1 citation statement)

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“…The methods in the above literature are summarized in Table 1. Niu [13] Ikram [14] Yan [15] Latif [25] Wang [17] Shapira [26] Lin [18] Zainudin [19] De [27] Shahin [20] Zhao [21] Zeb [22] Mohammed [23] Qi [24] Hu [28] Ning [29] Our Method…”

Section: Related Workmentioning

confidence: 99%

Malicious Traffic Classification via Edge Intelligence in IIoT

Wang,

Zhang,

Zang

et al. 2023

Mathematics

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The proliferation of smart devices in the 5G era of industrial IoT (IIoT) produces significant traffic data, some of which is encrypted malicious traffic, creating a significant problem for malicious traffic detection. Malicious traffic classification is one of the most efficient techniques for detecting malicious traffic. Although it is a labor-intensive and time-consuming process to gather large labeled datasets, the majority of prior studies on the classification of malicious traffic use supervised learning approaches and provide decent classification results when a substantial quantity of labeled data is available. This paper proposes a semi-supervised learning approach for classifying malicious IIoT traffic. The approach utilizes the encoder–decoder model framework to classify the traffic, even with a limited amount of labeled data available. We sample and normalize the data during the data-processing stage. In the semi-supervised model-building stage, we first pre-train a model on a large unlabeled dataset. Subsequently, we transfer the learned weights to a new model, which is then retrained using a small labeled dataset. We also offer an edge intelligence model that considers aspects such as computation latency, transmission latency, and privacy protection to improve the model’s performance. To achieve the lowest total latency and to reduce the risk of privacy leakage, we first create latency and privacy-protection models for each local, edge, and cloud. Then, we optimize the total latency and overall privacy level. In the study of IIoT malicious traffic classification, experimental results demonstrate that our method reduces the model training and classification time with 97.55% accuracy; moreover, our approach boosts the privacy-protection factor.

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