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.

Our framework may be thought of as a generalization of the synthetic control and synthetic interventions frameworks, where data is collected via an adaptive intervention assignment policy.

Let g[1](zx) be g(zx) and W[1] be W for notation simplicity. Consider each dimension's scalar output ofg(zx), and it is denoted asg p (zx) where p is an index of the output dimension. For each AL round, a target modelΘis trained via stochastic gradient descent(SGD) using IN examples in the labeled setSL (Lines 3-5). The initial learning rate of0.1 is decayed by a factor of 0.1 at 50% and 75% of the total training iterations. Owing to the ability to find the best balance between purity and informativeness, MQ-Net achieves the highest accuracy on every AL round.

Noisy labels are inevitable in large real-world datasets.

Our framework may be thought of as a generalization of the synthetic control and synthetic interventions frameworks, where data is collected via an adaptive intervention assignment policy.