Traffic-Adopted Confident Learning for Network Traffic

• Traffic-adopted Confident Learning is a label-noise mitigation module that refines SSL-generated pseudo-labels for network traffic classification, addressing class imbalance and calibration drift.
• It employs per-class quantile thresholding, calibration-aware weighting, and class-wise balanced retention to reliably filter noisy labels and enhance classifier performance.
• Integrated as the fourth stage in a six-step NTC pipeline, the method boosts accuracy significantly, achieving up to 98.76% on QUIC datasets with minimal labeled data.

Searching arXiv for recent and foundational papers on confident learning and network traffic classification to ground the article. Traffic-adopted Confident Learning (CL) is a label-noise mitigation component for network traffic classification that is integrated into a broader framework combining self-supervised learning (SSL), pseudo-label generation, and final supervised training on reweighted pseudo-labeled data. In the formulation reported in "Network Traffic Classification Using Self-Supervised Learning and Confident Learning" (Eslami et al., 27 Sep 2025), the method is designed for network traffic data with continuous and categorical attributes, and specifically adapts Confident Learning to class imbalance, calibration drift, and noisy SSL pseudo-labels. The resulting workflow targets high-accuracy classification under limited labeled data by first generating pseudo-labels from extensive unlabeled datasets and then refining those pseudo-labels through per-sample reliability estimation, per-class thresholding, calibration-aware weighting, and class-wise balanced retention (Eslami et al., 27 Sep 2025).

1. Conceptual definition and problem setting

Traffic-adopted Confident Learning addresses a specific failure mode in learning-based network traffic classification: pseudo-label noise arising when unlabeled traffic flows are annotated automatically by upstream SSL-based models. The motivating setting is network traffic classification (NTC), where traditional methods such as deep packet inspection and port-based identification are described as struggling with encrypted traffic and dynamic port allocations, while fully supervised learning depends on large labeled datasets that are difficult to acquire given the diversity and volume of network traffic (Eslami et al., 27 Sep 2025).

Within that setting, traffic-adopted CL is not a standalone classifier. It is a refinement stage applied after pseudo-label generation and before final classifier training. Its stated role is to enhance classification precision by mitigating the impact of noise in pseudo-labels produced from unlabeled traffic data (Eslami et al., 27 Sep 2025). The adaptation is domain-specific: rather than using a single global decision rule, it introduces mechanisms that account for skewed class distributions, heterogeneous confidence calibration, and the risk that minority classes may be eroded by aggressive filtering.

A common source of ambiguity is the abbreviation “CL.” In the network traffic classification framework, CL denotes Confident Learning (Eslami et al., 27 Sep 2025). In contrast, the traffic trajectory prediction literature also uses CL to denote continual learning, as in the Dynamic Gradient Scenario Memory approach for vehicle trajectory prediction (Lin et al., 2022). These are distinct methodological traditions despite sharing the same acronym. A plausible implication is that the “traffic-adopted” qualifier is especially important for disambiguation.

2. Placement within the end-to-end NTC pipeline

The reported framework organizes traffic-adopted CL as the fourth stage of a six-step pipeline. The preceding stages are feature extraction, SSL pretraining, and pseudo-label generation and fusion; the following stages are final classifier training and deployment (Eslami et al., 27 Sep 2025). The sequence is operationally important because the CL module consumes both pseudo-labels and out-of-sample predicted probabilities.

The feature extraction stage aggregates raw network packets into flow-level feature vectors containing continuous and categorical attributes. Two SSL branches are then trained on unlabeled traffic. One branch is an autoencoder (AE) that learns compressed representations with constraint-consistent reconstruction. The other is Tabular Contrastive Learning (TabCL), which learns representations invariant to class-conditioned, constraint-preserving perturbations and uses dual projection heads for heterogeneous feature types (Eslami et al., 27 Sep 2025).

Each branch predicts pseudo-labels on the unlabeled pool, after which their predictions are fused by a confidence or margin voting rule. If the AE and TabCL predictions agree, the common prediction is assigned. If they disagree, the prediction with higher confidence is chosen; if tied, greater margin or deterministic fallback is used. The paper states that this fusion marginally increases pseudo-label accuracy and precision, thereby improving downstream CL filtering (Eslami et al., 27 Sep 2025).

Traffic-adopted CL is then applied to the pseudo-labeled dataset. It estimates label reliability using out-of-sample probabilities, applies per-class quantile-based thresholds and calibration-aware weighting, and balances retained training mass per class according to estimated label cleanliness. The final classifier is subsequently trained on the reweighted pseudo-labeled set using a weighted, noise-aware Symmetric Cross-Entropy loss (Eslami et al., 27 Sep 2025). This architecture makes CL the central denoising and reweighting interface between SSL-generated pseudo-labels and supervised deployment.

3. Core mechanics of the traffic-adapted CL module

The first operation is self-confidence estimation. For each pseudo-labeled sample $(\mathbf{x}_i, \tilde{y}_i)$, out-of-sample predicted probabilities $\mathbf{p}_i$ are computed using $F$-fold cross-validation, with the requirement that the model producing $\mathbf{p}_i$ is not trained on $\mathbf{x}_i$. The scalar confidence score is then defined as

$$\mathbf{p}_i \in [0,1]^K, \qquad s_i = \mathbf{p}_i[\tilde{y}_i].$$

Here, $s_i$ is the confidence assigned to the pseudo-label $\tilde{y}_i$ (Eslami et al., 27 Sep 2025).

The second operation is per-class quantile thresholding. Rather than adopting a mean threshold, the method defines class-specific thresholds

$$t_j^{(q)} = \mathrm{Quantile}_q(\mathcal{S}_j), \qquad \mathcal{S}_j = \{s_i \mid \tilde{y}_i = j\},$$

with the hyperparameter $q$ chosen by stratified cross-validation (Eslami et al., 27 Sep 2025). This is presented as an adaptation to skew and imbalance in traffic data.

The third operation is calibration-aware per-sample weighting. The method measures classwise confidence dispersion via Median Absolute Deviation, denoted $\mathbf{p}_i$0, and computes a standardized margin

$\mathbf{p}_i$1

where $\mathbf{p}_i$2 is a tuned slope parameter. Sample weights are then assigned by

$\mathbf{p}_i$3

This formulation replaces hard filtering with a smooth down-weighting rule for samples that lie near or below their class threshold (Eslami et al., 27 Sep 2025).

The fourth operation is class-wise balanced retention (BRC). The method defines the confident joint matrix

$\mathbf{p}_i$4

and estimates the clean fraction of each observed class by

$\mathbf{p}_i$5

The target mass is set to $\mathbf{p}_i$6, and a scaling factor $\mathbf{p}_i$7 is computed using the current summed class weight $\mathbf{p}_i$8. Final per-sample weights are then given by

$\mathbf{p}_i$9

According to the paper, this aligns the retained effective mass per class with the estimated clean mass and addresses minority class erosion (Eslami et al., 27 Sep 2025).

Taken together, these operations define the traffic-specific character of the method. The adaptation is not merely threshold selection; it is a coupled procedure for reliability estimation, within-class normalization, and cross-class retention balancing.

4. Interaction with SSL pseudo-labeling and final optimization

Traffic-adopted CL depends directly on the quality of the pseudo-labels it receives. The upstream SSL branches are therefore part of its effective operating conditions. The AE branch pretrains on unlabeled data, then replaces the decoder with a classifier and fine-tunes on a small labeled set before predicting pseudo-labels. The TabCL branch performs contrastive pretraining with class-conditioned feature replacements and projection to satisfy domain constraints, discards the projection heads, attaches a classifier, fine-tunes on the small labeled set, and then predicts pseudo-labels for the unlabeled pool (Eslami et al., 27 Sep 2025).

The fusion rule joining AE and TabCL predictions is consequential for the CL stage because it determines the initial pseudo-label distribution and confidence profile. The paper states that pseudo-label quality directly impacts final accuracy, with the ordering fusion $F$0 TabCL $F$1 AE in the reported ablations (Eslami et al., 27 Sep 2025). This suggests that traffic-adopted CL is not intended to compensate for arbitrarily poor pseudo-labelers; rather, it is a denoising mechanism that benefits from stronger upstream representation learning.

After CL weighting, the final classifier is trained with weighted Symmetric Cross-Entropy (SCE). For each sample, the loss is

$F$2

where $F$3 is cross-entropy and $F$4 is its reverse, with $F$5 and $F$6 tuned by cross-validation (Eslami et al., 27 Sep 2025). The use of $F$7 makes the optimization explicitly noise-aware. In effect, traffic-adopted CL transforms pseudo-labeled traffic flows into a continuously weighted training set rather than a binary retained-versus-discarded subset.

The paper characterizes this integration as an end-to-end robust framework, from packet-level traces to deployed classifier, and emphasizes its fit for large, evolving, and imbalanced traffic data (Eslami et al., 27 Sep 2025). A plausible implication is that the CL module is best understood as a bridge between weak supervision and robust discriminative training.

5. Empirical characterization and reported performance

The framework is evaluated on three datasets: ISCX VPN-nonVPN, a self-generated dataset, and UCDavis--QUIC (Eslami et al., 27 Sep 2025). The reported findings are centered on label efficiency, robustness to pseudo-label noise, and comparative classification accuracy.

On the self-generated and ISCX datasets, the paper reports 96.29% accuracy using only 538 labeled flows. On UCDavis--QUIC, it reports 98.76% accuracy with 5% labeled flows per class (Eslami et al., 27 Sep 2025). The paper further states that the proposed method achieves superior accuracy compared to state-of-the-art techniques in classifying network traffic and that it outperforms strong supervised and SSL-only baselines on UCDavis--QUIC.

The ablation narrative is also central to the characterization of traffic-adopted CL. The paper states that each CL adaptation—quantile thresholds, logistic weighting, and BRC—incrementally improves robustness and accuracy over original CL or direct training on pseudo-labels. It also reports that removing CL or SSL pre-training sharply reduces performance, especially on minority or noisy classes (Eslami et al., 27 Sep 2025). These claims define the empirical role of the traffic-adapted CL modifications more precisely than the aggregate accuracy values alone.

The following summary table organizes the principal reported components and outcomes.

Component	Reported role	Reported outcome
Per-class quantile thresholds	Adapt to skew and imbalance	Improves robustness and accuracy over original CL
Logistic weighting	Avoid hard filtering; smooth down-weighting	Improves robustness and accuracy
BRC	Align retained mass with estimated clean mass	Addresses minority class erosion
SSL pseudo-label fusion	Improves pseudo-label reliability	Fusion $F$8 TabCL $F$9 AE
Full SSL + CL framework	Train with minimal labeled data	96.29% accuracy with 538 labeled flows; 98.76% accuracy with 5% labeled flows per class

Because the data attribute the performance gains to the combined SSL-plus-CL pipeline, care is required in interpretation. It would be inaccurate to ascribe the reported end metrics solely to the CL module. The stronger statement supported by the paper is that traffic-adopted CL contributes materially to robustness and final accuracy when embedded in the complete framework (Eslami et al., 27 Sep 2025).

6. Relation to adjacent notions of confidence in traffic-related learning

Traffic-adopted Confident Learning belongs to a broader family of methods that try to make prediction confidence operational in safety- or performance-critical settings, but it is methodologically distinct from confidence calibration through conformal prediction. In "Improving Prediction Confidence in Learning-Enabled Autonomous Systems" (Boursinos et al., 2021), confidence is handled through Inductive Conformal Prediction (ICP) on a triplet-network embedding, yielding set predictions with a predefined error rate controlled by a significance level and optionally refined through a sensor feedback loop. That framework is evaluated on a traffic sign recognition dataset and is described as computationally efficient, scalable to high-dimensional inputs, and executable in real time (Boursinos et al., 2021).

The distinction is substantive. In the ICP framework, confidence concerns whether a test example should yield a singleton set prediction, a multiple-label set, or no label at a chosen significance level. In traffic-adopted CL, confidence concerns the reliability of pseudo-label assignments in a training corpus, estimated via out-of-sample probabilities and converted into training weights (Eslami et al., 27 Sep 2025). The former is a prediction-set calibration method for autonomous perception; the latter is a label-noise refinement method for network traffic classification.

A second adjacency arises from the acronym collision with continual learning in traffic prediction. The Dynamic Gradient Scenario Memory approach addresses catastrophic forgetting in trajectory prediction by combining a scenario repository, traffic divergence measurement through conditional Kullback-Leibler Divergence, dynamic memory allocation, and a modified Gradient Episodic Memory algorithm (Lin et al., 2022). That problem setting concerns sequential scenario adaptation rather than pseudo-label denoising. The shared abbreviation “CL” therefore masks markedly different objectives: confidence-aware data curation in one case, and forgetting mitigation across tasks in the other.

This broader context clarifies the scope of traffic-adopted Confident Learning. It is neither a generic confidence calibration procedure nor a continual adaptation algorithm. It is a domain-specialized extension of Confident Learning for pseudo-labeled, imbalanced, heterogeneous network traffic data (Eslami et al., 27 Sep 2025).