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Xianwen Deng

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3 papers

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NeurIPS Conference 2025 Conference Paper

FlowRefiner: A Robust Traffic Classification Framework against Label Noise

Mingwei Zhan
Ruijie Zhao
Xianwen Deng
Zhi Xue
Qi Li
Zhuotao Liu
Guang Cheng
Ke Xu

Network traffic classification is essential for network management and security. In recent years, deep learning (DL) algorithms have emerged as essential tools for classifying complex traffic. However, they rely heavily on high-quality labeled training data. In practice, traffic data is often noisy due to human error or inaccurate automated labeling, which could render classification unreliable and lead to severe consequences. Although some studies have alleviated the label noise issue in specific scenarios, they are difficult to generalize to general traffic classification tasks due to the inherent semantic complexity of traffic data. In this paper, we propose FlowRefiner, a robust and general traffic classification framework against label noise. FlowRefiner consists of three core components: a traffic semantics-driven noise detector, a confidence-guided label correction mechanism, and a cross-granularity robust classifier. First, the noise detector utilizes traffic semantics extracted from a pre-trained encoder to identify mislabeled flows. Next, the confidence-guided label correction module fine-tunes a label predictor to correct noisy labels and construct refined flows. Finally, the cross-granularity robust classifier learns generalized patterns of both flow-level and packet-level, improving classification robustness against noisy labels. We evaluate our method on four traffic datasets with various classification scenarios across varying noise ratios. Experimental results demonstrate that FlowRefiner mitigates the impact of label noise and consistently outperforms state-of-the-art baselines by a large margin. The code is available at https: //github. com/NSSL-SJTU/FlowRefiner.

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AAAI Conference 2023 Conference Paper

Yet Another Traffic Classifier: A Masked Autoencoder Based Traffic Transformer with Multi-Level Flow Representation

Ruijie Zhao
Mingwei Zhan
Xianwen Deng
Yanhao Wang
Yijun Wang
Guan Gui
Zhi Xue

Traffic classification is a critical task in network security and management. Recent research has demonstrated the effectiveness of the deep learning-based traffic classification method. However, the following limitations remain: (1) the traffic representation is simply generated from raw packet bytes, resulting in the absence of important information; (2) the model structure of directly applying deep learning algorithms does not take traffic characteristics into account; and (3) scenario-specific classifier training usually requires a labor-intensive and time-consuming process to label data. In this paper, we introduce a masked autoencoder (MAE) based traffic transformer with multi-level flow representation to tackle these problems. To model raw traffic data, we design a formatted traffic representation matrix with hierarchical flow information. After that, we develop an efficient Traffic Transformer, in which packet-level and flow-level attention mechanisms implement more efficient feature extraction with lower complexity. At last, we utilize the MAE paradigm to pre-train our classifier with a large amount of unlabeled data, and perform fine-tuning with a few labeled data for a series of traffic classification tasks. Experiment findings reveal that our method outperforms state-of-the-art methods on five real-world traffic datasets by a large margin. The code is available at https://github.com/NSSL-SJTU/YaTC.

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IJCAI Conference 2022 Conference Paper

3E-Solver: An Effortless, Easy-to-Update, and End-to-End Solver with Semi-Supervised Learning for Breaking Text-Based Captchas

Xianwen Deng
Ruijie Zhao
Yanhao Wang
Libo Chen
Yijun Wang
Zhi Xue

Text-based captchas are the most widely used security mechanism currently. Due to the limitations and specificity of the segmentation algorithm, the early segmentation-based attack method has been unable to deal with the current captchas with newly introduced security features (e. g. , occluding lines and overlapping). Recently, some works have designed captcha solvers based on deep learning methods with powerful feature extraction capabilities, which have greater generality and higher accuracy. However, these works still suffer from two main intrinsic limitations: (1) many labor costs are required to label the training data, and (2) the solver cannot be updated with unlabeled data to recognize captchas more accurately. In this paper, we present a novel solver using improved FixMatch for semi-supervised captcha recognition to tackle these problems. Specifically, we first build an end-to-end baseline model to effectively break text-based captchas by leveraging encoder-decoder architecture and attention mechanism. Then we construct our solver with a few labeled samples and many unlabeled samples by improved FixMatch, which introduces teacher forcing, adaptive batch normalization, and consistency loss to achieve more effective training. Experiment results show that our solver outperforms state-of-the-arts by a large margin on current captcha schemes. We hope that our work can help security experts to revisit the design and usability of text-based captchas. The source code of this work is available at https: //github. com/SJTU-dxw/3E-Solver-CAPTCHA.

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