FlowRefiner: A Robust Traffic Classification Framework against Label Noise
Authors: Mingwei Zhan
Ruijie Zhao
Xianwen Deng
Zhi Xue
Qi Li
Zhuotao Liu
Guang Cheng
Ke Xu
Venue: NeurIPS 2025 poster ()
Keywords: Traffic Classification
Label Noise Learning
Robust Network Traffic Analysis
Primary area: applications

Abstract: 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.

FlowRefiner: A Robust Traffic Classification Framework against Label Noise
Mingwei Zhan', Ruijie Zhao?", Xianwen Deng', Zhi Xue Qi
Zhuotao Liu Guang Cheng? Ke Xu
Shanghai Jiao Tong University "Southeast University *Tsinghua University (mw. zhan, 2594306528, zxue)0sjju edu. edu. (ruijiezhao, , chengguang) edu. {qli01, zhuotaoliu, xuke)@tsinghua edu.cm
Abstract
Network traffic classification is essential for network management and security. 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 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, propose FLOw REFINER, a robust and general traffic classification framework against label noise. FLOWREFINER consists of three core components: traffic semantics-drin noise detector, a confidence-guides label correction mechanism, and cross grannulrrgtgt robust classifier. First, the noise detector utilizes traffic semantics extracted from pre-trained encoder to identify mislabeled flows. Next, the confidence-gue guided label correction module fine-tunes label predictor correct noisy labels and construct refined flows. Finally, the cross-granin gaassgrauulartty 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 -he----fhhhhheeeeee baselines by a large margin. The code is available at /https://github com//SSI--SJTU/Fooeeee. iner
Introduction
Network traffic classification is fundamental task for the management, security, and optimization of modern networks. It enables critical capabilities such as identifying malicious behaviors (1, 2 K) enforcing quality
(QoS) policies (4 (), and monitoring application usage (7). It can also support user-centric analysis such profiling in social network applications by fine-grained inference of user actions (8, 9.. As network applications and protocols continue to evolve, traffic data have become increasingly complex, reflecting not only protocol interactions but also diverse user behaviors and service patterns (10). These complexities make automated traffic classification both more important and more challenging (11 13).
In recent years, deep learning (DL) algorithms have emerged powerful tools for traffic analysis for handling such complexity (14 [S] I6; I8). By leveraging large-scale labeled raw traffic data, DI--based models can automatically extract discriminative representations and achieve accurate classification performance. However, the success of DL-based methods heavily relies on the quality
*Corresponding author.
Prooect advisors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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of labeled training data, and their performance can degrade significantly in the presence of label noise (9).. On the other hand, label noise, caused by incorrect, ambiguous, or inconsistent annotations, is especially critical issue in traffic analysis tasks. Erroneous labels can hinder generalization in real-world scenarios and lead to severe consequences, such as failing to detect malicious traffic as suspicious or misclassifying latency-sensitive applications in QoS enforcement. Unfortunately, label noise is especially common in realistic traffic due to the complexity and variability of real-world environments and the flaws of automated labeling techniques (00 21). Though labels of lab-generated traffic could be refined through controlled environments and manual annotation processes, the label quality of realistic traffic is still challenging since the automated labeling is prone to errors (22).
Despite its significance, the issue of label quality is rarely noticed and discussed in existing DL-based traffic classification methods. Only few studies (23/(4) explicitly target label noise in the context of malicious traffic, a specific subfield of traffic analysis focused primarily on intrusion detection. However, these approaches can hardly generalize to general traffic classification tasks, some (23) are limited to binary classification, while others (24) rely on strong does not hold in more diverse traffic scenarios. In addition, though various methods (25) 26 27 28
malicious separability that
have been developed to address label noise in other fields (e.g., computer vision), they still struggle in traffic classification due to the inherent structural complexity and obfuscated semantics of traffic data. Thus, dealing with label noise in general traffic data remains a crucial and unresolved issue.
In this paper, we propose FLOWREFINER, a robust and general traffic classification framework against label noise. Unlike existing approaches from computer vision or malicious traffic detec- tion, FI.OWREFINER is tailored to the unique characteristics of traffic data and supports general classification across diverse scenarios. FLOWWREFNNER effectively detects, corrects, and classifies traffic with noisy labels in a uniform framework, achieving high performance without relying on high-quality labeled data, which are costly and difficult to obtain in increasingly complex network en- vironments. Moreover, by accurately identifying mislabeled flows, LOWWEFINERR provides valuable feedback to assist network administrators and annotators in improving traffic data quality. Specifi- cally, FLOW REFINER consists of three key components: traffic semantics-driver noise detector, a confidence- confidence-guided label correction mechanism, and a cross- granularrt robust classifier. First, we deploy a pre-trained traffic encoder to extract latent traffic semantic representations for noisy flow isolation. Then, the majority labels within each cluster guide the division of flows into clean flows and noisy flows. Next, the confidence-guided label correction refines the noisy flows by fine-tuning& traffic label predictor on clean data and correcting mislabeled samples based on predicted confidence scores, thus reintroducing the corrected noisy flows into the clean flows to form the refined flows. Finally, our cross- cross-granularity robust classifier integrates both flow-level and packet-level classification, enabling the model capture generalized patterns rather than focusing on noise-driven isolated features. It ensures robustness and improves classification performance even in the presence of noisy labels. In summary, our contributions are as follows:
We introduce FLOWREFINER, a robust method for general traffic classification that effectively detects, corrects, and classifies traffic with noisy labels. To the best of our knowledge, this is the first work to address the label noise issues across diverse traffic classification tasks.
We propose traffic semantics-driver noise detector. It leverages the traffic semantics extracted by the pre-trained traffic encoder to detect outlier labels as noise, where the raw flows are divided into clean flows and noisy flows.
We fine-tune a traffic label predictor based on isolated clean flows to perform confidence-guidee label correction of detected noisy flows. The correction results can assist network administrators and annotators to improve traffic label quality.
We design a grass-granularity robust classifier that integrates both flow-level and packet-level classification tasks to improve robustness, preventing the encoder from overfitting to noisy labels.
We evaluate FLOWREFINES on four real-world traffic datasets with different noise ratios. Results show that our method can achieve robust traffic classification against label noise and significantly outperforms the of-te-of-thh-art baselines.
2
Related Work
Traffic Classification Methods. In traffic analysis, traditional rule-based methods initially relied on fixed attributes like port numbers and protocols to classify traffic. However, with the rise of encryption
2

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Class
Noie
Vorger
Torger
Teature
Vectors
Packets
Enconler
Tmfffic
Clean
Prodicter
Labe
Refined
Traffic
Munual Antrtated
Flows
Traffic
Mode
Kandeen
Wrigght
Outfen
Seles
Noiey Flows
Prodlct
Lahrl
Chasoifer
Traffic
Pocket
Packet-levll
Fime-gmined Clusseing
Conlidence
Traffie Semuntlics Driven Noise Detector
Confidenne--Gaili Labe Carrection
Cros-Grannlarily Robest Classifier
Figure 1: The overview of FLOW
protocols such SSL and TLS, their effectiveness has gradually decreased because these protocols conceal key traffic characteristics (29) 7).. To overcome these limitations, machine learning (MI)
techniques, including support vector machine (SVM) and random forest (RF), leveraged statistical features from traffic data to automate feature selection and improve classification accuracy (30, (7). Although these ML-based methods marked significant advancement, they heavily depend on handcrafted features, which limits adaptability across various traffic types (13).
Deep learning (DL) methods have brought shift towards automatic feature extraction from raw traffic data, with convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers being widely adopted for learning hierarchical representations (16) 14). Recent arts have also explored pre- tre-training techniques, with PERT and ET-BERT (1 applying NLP-inspired
BERT models (33) encrypted traffic classification, and Flow-MAE (34,, YaTC (35) using masked
autoencoders (MAE) (36) to improve robustness in feature extraction under encryption. However,
they particularly prone to label noise, their high capacity allows them to overfit incorrect labels
39: 40) have noticed the label noise issue in deep learning-bai based intrusion detection systems, and propose corresponding methods for malicious traffic. However, they lack generalizability to more general traffic classification tasks. It is urgent to propose an effective method to resist noisy labels for general traffic analysis tasks.
337 Some works (24: 38
Label Noise Learning Methods. Various methods have been developed to address label noise in other fields such computer vision. A series of approaches like Generalized Cross-Entropy (GCE) (27) and Symmetric Cross-Entropy (SCE) (28) modify traditional loss functions to better handle noisy labels. GCE combines cross-entropy with mean absolute error make learning more noise-resistant, while SCE balances penalization of misclassified samples. In addition, some works (26) (25) investigate filtering noisy samples that rely on the loss value distribution. On the other hand, data augmentation technique has been wild used against label noise. As classic robust augmentation, Mixup (4T) applies interpolation between samples generate robust training data. But it struggles when applied to the structured nature of encrypted traffic flows, where such interpolations may not produce meaningful results. Recent methods like Manifold DivideMix (42), which incorporate
contrastive learning, offer improved robustness to severe noise. However, they rely on well-defined meaningful data augmentation, which is hardly performed in structured traffic data, limiting their ability to handle label noise within traffic flows.
In summary, while these label noise learning methods offer powerful mechanisms in other fields, the traffic classification still lacks effective solutions that can robustly handle noisy labels. To fill this gap, we propose FEONRE robust framework specifically designed to detect, correct, and classify traffic with noisy labels, offering a comprehensive solution for label noise in general traffic classification.
3 FLOWREFINER
We introduce FLFORREFNEER robust method for general traffic classification that effectively detects, corrects, and classifies traffic with noisy labels. Our framework starts with traffic semantics extracted by the advanced pre-trained encoder could reflect the similarities and diversity of flow characteristics,
3

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providing a basis for detecting label-noisy flows (detailed in Sec. 3.1. After that, predictor is fine-tuned via detected clean flows to correct noisy labels and construct refined flows with low-level label noise(detailed in Sec. (.2). Finally, traffic classifier with cross-gras cross-granularity structure is designed to perform robust general traffic classification against label noise (detailed in Sec. 3.3)
3.1 Traffic Demantics-Driveer Noise Detector
Traffic semantics-driven noise detector aims to identify and isolate noisy labels in traffic data effectively. It achieves effective noisy flow detection by leveraging the traffic semanties extracted from the flows and combining their similarity with label distribution.
Traffic Semantics Extraction. We first build a traffic cemantics-orienee encoder via the pre- training paradigm extract the latent representation of traffic data. Benefiting from the learned semantics from the unlabeled traffic data in pre-training, the pre-trained traffic encoder can effectively extract traffic semantics (32), while remaining immune interference from potential noisy labels.
We adopt the Masked Autoencoder (MAE) (36) style pre-training paradigm to enable the encoder to capture the traffic semantics by randomly masking input and reconstructing the missing portions (which is further detailed in Appendix A). In addition, the encoder deploys the Transformer archi- tecture the backbone for feature extraction. Formally, let X { NN represent the dataset, where z; is an individual flow sample. The pre-trained encoder, denoted as fene(-), processes each sample xt and produces d-dimensional feature vector: 2; = (enc(ri); 2j E Rdd
Noise Detection based on Traffic Semanties. The distances between traffic semantics embed- dings can effectively reflect the similarities in flow content and function. These similarities have the potential to highlight instances of label noise, as similar flows with discrete labels are likely candidates for noisy labels. However, as we visualized via t-SNE Figure [ since the traffic semanties have not yet been aligned with the correct labels, there are two main concerns that should be considered during noise detection: There are two main challenges in detecting label
Clasr
Clasd
Extraeted Traffie Semantics via Pre-irsining
Simllar Semaattis Diverse Classes
Döverse Semuaatic from the Sams Class
Figure 2: The t-SNE visualization of the traffic semantics.
noise via similarities: (1) Traffic data in the same class may not exhibit consistent behaviors, forming diverse compact clusters in the latent space; (2) Traffic data from different classes may appear se- mantically similar, leading to groups of mixed-category flows. Therefore, we designed fine-grained clustering and defined majority labels for each cluster to address the above issues and detect noisy labels.
To capture the different behaviors inside the same class, we further cluster the semantics similarity between flows with more fine-grained level than categories. Specifically, we partition extracted flow samples into K clusters, according to their extracted feature vectors {z1, . ZN) } by the K-means algorithm, where each flow sample grouped based on its semantic proximity to other samples. Especially, the number of clusters is defined as K X C, where C' represents the total number of classes and an integer hyperparameter introduced to control the granularity of the clustering. The objective function for K- K-means clustering is to minimize the sum of squared distances between the feature vectors and the corresponding cluster centroids:
N
K K(2 E CW)ll -1x11
min ΣV
where b4 is the centroid of the k--th cluster, and (F C2) is an indicator function that assigns 2; to the nearest cluster Cs. Subsequently, flows within the same cluster share the most similar semantics, and the presence of inconsistent labels within each cluster is regarded as potential noise.
Considering the potential similar activity of flows from different categories, we define the majority labels for each cluster to represent the inside overlapped true classes. Let L be the set of labels for the samples in cluster Cg. The majority label majorr is determined as the top m most frequent labels

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in the cluster. Samples with labels that are not among the majority labels major in their cluster are treated as statistical outliers, and their labels are considered noisy.
Finally, we detect and divide the raw flows into a clean flow set and a noisy flow set:
Delean = ((1)))) x4 E CR.yi y6 E Li major Dnoisy = {(11. Vi) 24 E Cki Vo & Li
major
where yi is the raw label for sample wi, and Li _ the majotity label of cluster Ck. The clean flows Delean can be used to support further training, while the noisy flows Droisy can be flagged and reported the manager or annotator for review.
3.2 Confidence-Guided Label Correction
Through the semantic-driven noise detector, most noisy flows are identified and isolated from the clean flows according to their aabel-independent traffic semantics. Furthermore, this clean flow set can provide valid supervision to further correct the label distribution of the noisy flows and expand the available training data. For this purpose, design the confidence-guiic guided label correction module to correct flow labels the noisy flow set based on prediction confidence.
Traffic Label Predictor Fine-tuning. In this stage, aim to fine-tune the latent representations of flows to align with the labels of selected clean flows Delean. thereby obtaining a confidence- aware traffic label predictor that can assess the category of selected noisy flows Daoisy based on confidence scores. Formally, let Xelean {œ1, [ #.. .N.. be the set of clean flow samples in Deleans and Velean = {y11 V2...... .N. be their corresponding labels. Then, we leverage the supervised learning paradigm to fine-tune our label predictor fz) which consists of the pre- trained encoder and an added classification head, aiming to minimize the cross-entropy loss: CCE
-i y: (pg(((((())))
Our well- label predictor provides confidence scores, p: = max(fprd(am which represent the predictor's 's certainty regarding the predicted class. These confidence scores serve the basis for further label correction of noisy flows.
Noise Label Correction. To refine the available flows with label noise and avoid losing valuable diversified information, we correct the original labels of noisy flows based on the predicted confidence scores. Specifically, for each noisy flow sample and its label (I,,. yi) E D Dosssy. the predictor outputs predicted label Ú, along with its confidence score = max (z;)) which is the highest softmax probability from the label predictor. Based on these confidence scores, we refine the labels of noisy flows into two categories (as examples in Figure of the Appendix B):
(1) High- Confidence Flow Labels (p, 2 Tx:): These samples have high confidence in their predicted labels and are thus assumed to share a similar distribution with the clean set. Thus, we correct their labels with the predicted labels i and merge them into the refined flows.
(2) Con--Confieecce Flow Labels (p: I T)): These samples have low confidence and likely lie outside the semantic distribution of the clean set. Since they are difficult for the classifier to categorize, we retain their original labels and introduce them to the refined flows to maintain diversity and expand the semantic distribution of samples.
Note that Tr and tæ represent the thresholds for refining high and low confidence flow labels, respec- tively. Combined with the clean flows, the obtained refined flows Drefined contain fewer noisy labels but preserve a more representative semantic distribution the raw data, including samples that lie outside the initial clean flows's boundaries. The final refined flows are formulated as follows:
Dretied = Detean U (x,, (t) Ip: > Th U (( vi) ps < <h.
(3)
where the Deteun represent clean flows from the semantic-driven noise detector, the x; is each sample of noisy flows Dunieys ÚJ is the predict label, y: is the raw label, and p; is the confidence score.
3.3 Cross-Granularity Robust Classifier
Based on the refined flows with low-level label noise, we perform traffic encoder fine-tuning for robust traffic classification. Due to strong fitting capabilities, deep learning models are prone to memorize
5

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mislabeled samples themself, rather than capturing generalizable patterns (43 44). To avoid the encoder memorizing specific flows with false labels, we propose cross- gesssuraullrrty robust classifier that integrates both flow-level and packet-level traffic classification tasks. Thus, our classifier can capture generalized patterns that are valid on both tasks, rather than attention to isolated invalid features that are misdirected by noisy labels.
The robust traffic classifier contains two parallel shared weight encoders and classification heads, which could be formulaically treated as a single encoder and denoted as fene and fhead. The structure and explanation of the shared- weight classifier that can handle both flow and traffic samples are detailed in Appendix C The encoder is also based on the Transformer and loads the parameters pre-trainet by MAE. It first processes each flow 2; by dividing it into sequential packets then selects one packet x! randomly to serve as the other input sample:
RandomSSeeed ((pac) 1.. [x;))))
(4)
Next, the flow sample 2; is inputted to the encoder and linear heads to generate Ú: a flow-level prediction of the label. Following the above process, the flow classification task is performed with the flow sample 2; and label yi from the refined flows by minimizing the cross-entropy loss. Then, the packet classification task is trained subsequentially during each epoch. Similarly, the randomly selected packet sample is inputted to the encoder and classification head, and obtains the packet level prediction v." By associating packet sample x? with the flow label y, via the cross-entropy loss, the classifier performs packet classification and learns traffic patterns on another granularity.
By the cross- cross-granul granularity classification tasks, the classifier utilize both global (flow-level) and local (packet-level) features within the refined flows, providing more varied input against label noise. Specifically, by introducing randomness in the packet selection, the classifier obtains different sample variants in each epoch, thus avoiding the memorization of specific flows with false labels. In addition, the trained tross-granularigy robust classifier can serve for both flow-level pecket-level traffic classification under the label noise according to the practical needs, bringing not only robustness but also flexibility.
4 Experiments
4.1 Experiment Settings
We conduct our experiments on four real-world traffic datasets: ISCXVPN (45), Cross- Platform (46), USTC-TFC (I6), and Malware (47), each representing different traffic scenarios. Each dataset sample in our experiments corresponds to a network flow, which is obtained via session-awan splitting according to the standard 5-tuple (source IP, destination IP, source port, destination port, and protocol). FLOW REFINER, we process each flow into formatted matrix via the MFR algorithm (35). To comprehensively evaluate the robustness of our methods against label noise, generate noisy datasets for each scenario with different noise ratios (5%, 10%, 20%, 40%, 60%). The details of these datasets shown in Appendix D
Datasets.
Baselines. To evaluate the performance of FLOW RERINEE, we compare it with six state-of-the-art traffic classification methods. These include two traditional MI. and DL-based methods, App- scanner (7) and FS-Net (14); three advanced pre-training methods, ET-BERT (32), MAF (34), and YaTC (35); and the malicious traffic label noise method, MCRe (24). In addition, we introduce three packet classification baselines (48 16 32) and six general label noise learning baselines. CE, LSR SCE Co-teaching (26), and Dividemix (25). further extend the
(49), Mixup 41) GCE evaluations.
Implementation Details. the training stage, we set the batch size as 64, the epochs as 20, and the learning rate as 4 10 with the Adam W optimizer (50). In noise detection, we set the parameter of clustering granularity as n = 5, and the top m most frequent labels in the cluster are defined as the majority labels. The high and low confidence thresh- olds are setted TA = 0.9 and = 0.7. All experiments are implemented in four NVIDIA GeForce RTX3090 GPUs with PyTorch 1.9.0. We summarize the hyperparameter settings in Appendix F

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Table 1: Performance Comparison with Traffic Classification Baselines under Different Noise Ratios.
Datasct
Noise
Appicunner
FS-Net
ET-BERT
MAE
YaTC
MCRe
Ours
Acc
F1
Acc
Acc
Acc
Acc
F1
Acc
F1
Acc
Fl
5%
81.49
82.12
67.97
88.05
86.12
86.11
91.04
91.08
92.44
92.33
83.01
83.63
93.67
93,34
79.22
79.63
85.24
89.81
89.03
91.04
90.48
ISCXVPN
20%
77.27
77.95
82.57
82.57
78.91
79.07
79.26
79.29
82. 64
82.49
79.88
80.64
90.86
90.15
40%
73,70
73.85
74.37
75.08
59.92
61.78
63.62
64.21
66.43
67.15
76.56
76.62
85.06
84,19
60%
57.14
57.89
56.40
58.24
34.79
36.21
42.00
43.19
43.23
45.37
69.92
70.55
73.29
72,90
5%
68.00
67. 34
61 88
61.04
98.97
98 99
97.67
97.
98.91
98.
67 66
67.59
99,71
99.71
39.61
98.39
95.15
95.24
CrossPlatform
20%
62.53
62. 39
57.
07
941, 46
94.44
90.67
90.75
93.51
93.58
61 A8
61.14
98.76
98.75
40%
55,32
55.51
50.54
50.49
78.42
79.30
73.98
75.12
80.47
80,92
53.91
52.44
95.34
95,20
60%
43.29
44.76
39.76
56.26
57.22
58.94
43.44
40.76
84.29
5%
62,40
57.64
87.50
87.61
95.03
944, 64.98
94. 82
94.82
94,62
94,55
93.75
94.91
96.07
96.02
10%
93.79
93,99
USTC-TFC
20%
62.65
59.95
84.76
84,70
92.333
92.33
86.
86.46
89.86
89.75
91.02
92.91
94,20
93.93
40%
56,26
50.56
77.89
777.52
81.98
$1.669
74.12
73.82
80.54
80.11
89.06
91.41
91.72
91.69
60%
51.73
50.98
70.62
58.59
58.09
47.62
48.11
56.31
56.57
75.78
75.02
78.71
5%
18.16
77.86
77.81
77.90
85.95
86.01
92.57
92.59
93.11
93,11
65.62
66.77
93.38
93.33
10%
76.36
76.71
84.46
90
63.28
91.28
Malware
20%
76.43
76.14
75.78
75.83
77.83
78.03
81.49
81.43
83,51
83,50
62.50
64.19
88.24
88.03
40%
71.26
70.67
71.40
71.08
61.35
61.49
65. 27
65.39
67.97
68.49
57.23
58.83
83.51
83,33
60%
63.02
62.71
57.81
57.13
42.43
46.08
47.16
46.35
55.47
56.21
73.11
72.61
4.2 Comparison with Baselines
Traffic Classification Baselines. The performance of our method and other traffic analysis methods on the four traffic datasets under different noise ratios is shown in Table Overall, the performance of all baselines degrades significantly as the label noise rate increases, which highlights the substantial impact of label noise on DL-based methods. Traditional models such as Appscanner and FS- are particularly vulnerable, showing rapid performance drops even under moderate noise. Although advanced pre-training methods such as ET-BERT, MAE, and YaTC perform well under low-noise settings such as 5% and 10%, their robustness deteriorates noticeably as the noise level increases. MCRe, which is designed for handling label noise in malicious traffic, shows strong robustness on the USTC-TFC that includes multiple benign and malicious traffic categories. However, its performance degrades significantly on other general classification tasks, such as VPN or mobile app traffic, as well as on the Malware dataset, which contains only recent malware families, due to its reliance on distinct menign-malicious separability.
We can observe that FLOW REFINER consistently outperforms all baselines across datasets and noise levels. Our traffic semantics driven noise detector and confidence-guiin -gided label correction can validly isolate noisy labels and refine the raw flows. Then, the cross- goans--ganularity robust classifier can capture meaningful hierarchical semantics and avoid remembering specific noisy flows. As a result, our method consistently achieves accuracy and F1 scores exceeding 70% even at a 60% label noise ratio, where the majority of training samples are incorrectly labeled. Besides, FLOWREFINER also shows better packet-level classification performance compared to the advanced packet classifiers in Appendix G It can be concluded that our method provides a robust traffic classification framework against label noise to achieve superior performance on various traffic datasets under noisy label conditions.
Label Noise Learning Baselines. We further reproduce six general label noise learning methods from other fields on traffic data with consistent encoders for fair comparison, as illustrated in Table 2 LSR and SCE show almost no improvement over CE (i.c., the baseline), while Mixup and GCE can handle label noise relatively effectively due to the interpolating and adaptive loss design. More advanced methods, such Co-teaching and Dividemix, achieve competitive performance. However, Co-teaching relies on a fixed forgetting rate to select small-loss samples as noise, which could not satisfy various noise conditions. DivideMix, on the other hand, uses a Gaussian mixture model based on loss value distribution to distinguish noise, but it fails when clean or noisy samples are too few to support reliable modeling. In contrast, our method detects noisy flows by modeling traffic semantics rather than relying on loss value distribution, making it more aligned with the nature of traffic data. The results show that FLOWRFFFNER achieves high performance under different noise ratios and leads the noisy label learning baseline by a large margin.
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Table 2: Comparison of F1 Scores with Label Noise Learning Baselines under Different Noise Ratios.
Method
ISCXVPN
CrossPlatform
USTC-TFC
Malware
5%
10%
20%
40%
60%
5%
10%
20%
40%
60%
5%
100
20%
40%
60%
5%
10%
20%
40%
60%
66.67
52.94
LSK
75,65
94.36
90:45
58.73
55.27
82.91
Mixup
79.33
77.79
79.10
66.46
48.31
90.83
89.05
36.58
78.48
61.59
91.89
90.29
87,80
13.77
64.12
89.27
88.12
82.22
73.33
62.63
OCF
75.39
74.59
71.80
65.08
49.05
93.29
92
92.
90.19
79.69
82.28
80.58
78.42
77.00
68.01
86.22
81.76
80.15
74.39
6446
SCB
77.04
73.80
73.44
59.49
40.68
94,04
93.41
87.12
76,82
00.33
86.74
85.10
85.04
73.84
56.54
58.87
83.90
71.93
67.79
56.61
Costraching
78.24
84.14
87.65
76.69
55.89
62.29
87.83
92.04
83.93
62.61
86.24
88.16
81.21
81.21
57.22
86.85
88.20
90.80
60.29
Dividemix
78.45
82.41
70.34
Dur
93.34
90.48
90.15
84.19
72,90
99.71
99.55
96.02
95.60
78.71
99.33
72.61
Table 3: Comparison of F1 Scores Class-dependent
Datection Ratto 186)
100
Noise Detection
Correction Redio (%5
Noise Comeection
Real-worts Evoluaion
81.92
Noise Scenarios.
60
15CRVPN
ISCKVPN
CrossPlattom
USTC-TFC
tel 8
Cross CrossPiatfrrm
USC
30 23
Method
|ICXVPN
USTC-TPC
Malware
Malware
47:31
YaTC 77.01%
86.01%
Noise Ratio (%) (2)
Noise Ratio
CICIDS2017
Co-teaching
76.80%
76. 17%
(6)
[C]
DivideMix
74.14%
85.94%
Mixup 69.86%
78.56%
Figure 3: Performance of (a) noise detection, (b) noise correction, and (c) real-world evaluation.
80.97%
88.40%
Class-dependent Noise Evaluation. Class-dependent noise occurs when the probability of label corruption not uniform across classes, meaning certain classes are more prone having mislabeled samples than others. We perform the evaluation on ISCXVPN and USTC-TFC with different ratios of class-dependent noise, where categories with similar patterns are more likely to be mislabeled as each other. The average F1 scores among noise ratios of FLOW REFINER and optimal baselines are shown in Figure 3 The complete results of all baselines and noise ratios are detailed in Appendix [H] It can be observed that all methods perform worse compared to the complete random noise setting, since the class-dependent noise leads to more severe category confusion and shifted decision boundaries. Note that our method can still achieve significant performance advantages over the optimal baseline, demonstrating better robustness under different noise types.
4.3 Label Noise Evaluation
Noise Detection Performance. We evaluate the performance of the traffic semantics-driven noise detector in Figure (a). The detector consistently identifies a large portion of noisy-labeled flows across all datasets, with the detection ratio, i.e., the proportion of detected noise among all noisy samples, remaining above 75% even at 60% noise. This robustness is particularly valuable in security- sensitive tasks such as malware detection, where undetected noise may lead to critical failures. These results confirm the detector's effectiveness in isolating noisy flows and supporting reliable traffic annotation and management.
Noise Correction Performance. The confidence-guiie guided label correction is responsible for cor- recting the labels of the previously selected noisy flows. The correction ratio, i.c. accuracy of the noisy flow correction, is shown in Figure (b). It can be seen that method can accurately select and assign the correct labels to noisy flows based on prediction confidence, consistently achieving a correction accuracy of over 80% in most scenarios. Particularly the CrossPlatform and USTC-TFC datasets, labels are almost all correctly assigned to the selected noisy flows, demonstrating the notable performance of the label predictor in this module.
Real-Wortd Noisy Dataset Evaluation. Previous studies 20 21 have revealed significant label noise in the CICIDS2017 dataset (51), and provide corrected version with revised flow labels. Based on the dataset, we conduct real-world evaluation, where the training set retains the original noisy labels, and the test set is relabeled according to (20). MCRe, the other traffic label noise method and the only baseline capable of both detecting and correcting label noise, introduced to the comparison. As shown in Figure 3 (c). our method successfully identifies 84.61 % of the noisy flows in training set, significantly outperforming MCRe. Furthermore, the accuracy of our noise detection is 81.9 81.92% achieving high agreement with the expert relabeling.
8

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Table 4: Ablation Study of F1 Scores on The Four Traffic Datasets. The abbreviations explained as follows: TSND: Traffic Semantics- Domantics-Driven Noise Detector, CLC: Confidence-Guidee Label Correction Module, CRC: GrasuGrraullrrty Robust Classifier.
Method
ISCXVPN
CrossPlatform
USTC-TTC
Malware
5%
10%
20%
40%
60%
10%
20%
40%
60%
10%
20%
40%
601
5%
10%
20%
40%
60%
Dars
90.48
90.15
84.19
72.90
99.71
98.75
95.20
95.60
91.69
78.71
83.33
72.61
wio TSND
93.00
88.
84.10
64.27
48.60
97.09
95.
88.52
68.84
46.85
95.99
93.87
91.43
75.
48.95
93.14
92:27
84.14
67.65
47.42
wio CLCC
88.85
85.09
80
12
77.76
69.71
92.11
90.84
89
69.69
86.35
78.01
94.88
93.26
92. 78
89.
77.59
80.12
79.15
79.11
74.78
65.29
Wdo CRC
91,33
88.39
85.05
78.42
65.16
97.99
98.07
95.41
X8.23
70.04
95.55
95.40
93.38
90.01
74.60
86,95
89.56
83.85
78.68
63,87
4.4 Ablation Study
The ablation study is conducted to evaluate the contribution of each component in FLOWREFINER As shown in Table the performance declines consistently on all traffic datasets when any of the key components is removed. In particular, the removal of the traffic semantics-driven noise detector (TSND) results in the most severe performance degradation under high noise ratios. For example, on the ISCXVPN dataset with 60% noise, removing TSND leads to a significant drop in accuracy from 73.29% to 47.62%. Similarly, on the CrossPlatform dataset. accuracy drops from 84.33% to 44.89%. This highlights the critical role of the TSND in accurately identifying and isolating noisy flows, which ensures that the subsequent components operate with more reliable data. When the confidence-guu guiied label correction (CLC) is ablated, the performance also degrades, suggesting that this component can effectively against label noise by handling difficult cases and expanding the clean set. Note that our method has less performance degradation on the USTC-TFC dataset compared to other datasets under the ablation of CLC. This is because our noise detector has detected most of the noisy flows in USTC-TFC according to Figure 3 offering highly clean flow set for traffic classifier training. Finally, the removal of the structure of cross pross-granularity robust classifier (CRC) results in stable performance reductions under all datasets and noise ratios. It proves that the joint task of both flow and packet classification could improve the robustness under different noise ratios.
4.5 Discussions
The Impact of Parameters. We investigate the effect of two key parameters in our noise detection module: the clustering gramularity n and the majority label count. The clustering granularity n defines the total number of clusters K = n x C, where C' is the number of classes.
Score
It controls how finely label- -ndeeedddden semantics are modeled. As shown in Figure 4 on the ISCXVPN dataset, increasing n from 1 to 5 notably improves the F1 score. A setting of optimal performance by balancing semantic sensitivity and robust- ness, whereas higher values (e.g.. n 7) lead to over-segmentation and misclassification of clean samples as noise. The majority label
75
5%
40%
10%
60%
achieves
20%
3
5
7
Clustering Granularity
count determines how many of the most frequent labels in each cluster
Fl Score (9))
are retained as clean. Using only the top-1 label (count = 1) can misclassify semantically similar clean flows as noise, while larger
5%6
40%
counts may retain actual noisy samples. As shown in Figure a count of provides the best trade-off across noise ratios by tolerating intra-cluster semantic variation without sacrificing detection precision. Overall, both parameters play a critical role balancing noise detec- tion sensitivity and robustness, with n = 5 and majority label count 2 yielding consistently strong performance across settings.
10%
60%
20%
2
Majority Label Count
Figure 4: Parameters Impact.
Limitations. As a deep learning- learning-based method, FLOW REFINER requires substantial computational resources for efficient training, preferably with GPU acceleration. Our framework currently takes about 3 minutes on an RTX 3090 GPU with 2.79 GB of memory for training. In future work, we will continue to optimize the training pipeline to reduce computational overhead and improve accessibility, while also leveraging ongoing progress in lightweight inference and dedicated hardware accelerators, which are expected to further enhance the practicality and scalability of FLFWWREFINER in real-worde deployments.

---

5 Conclusion
In this paper, we proposed FLOWREFINER, a robust framework for general traffic classification under noisy label conditions. Our method addresses the significant challenges of label noise, including its detection, correction, and robust classification, thus reducing dependency on high-quality labeled data. Through the integration of a semantic-driven noise detector, confidence-guided label correc- tion, and cross-granule pross-granullarty robust classifier, FLOWREFINER effectively leverages noisy traffic to improve classification performance. Our experimental results across various datasets and scenarios demonstrate the ability of
to outperform state-of ofate-of-the-arr the-art methods in both accuracy and resilience to noise. This framework provides valuable solution for enhancing general traffic analysis, particularly in environments where high-quality labels are difficult to obtain, and sets the stage for future advancements in traffic classification under noisy conditions.
Acknowledgement
The authors thank the anonymous reviewers for their valuable comments and suggestions that helped improve this paper. This work was supported in part by China National Funds for Distinguished Young Scientists under Grant 62425201; in part by the National Key Research and Development Program China under Grant 2023YFB3107 100; in part by the National Natural Science Foundation of China under Grant 62502089, Grant 62172093, Grant 62132011, and Grant U22B2025; in part by Basic Research Program of Jiangsu under Grant BK20251 1353; in part by Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China under Grant JYB2025XDXM114 14; and in part by Beijing-Tiamjje Hebei Natural Science Foundation Cooperation Project under Grant 25JJJJC0003.
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Appendix
A Building of the Traffic Semantics-oriented Encoder
Figure 5 depicts the process of building the traffic semantics-oriented encoder with the pre-training paradigm of a masked autoencoder (MAE). Initially, the network traffic data captured from Peap files undergoes preprocessing extract and resize the flow content, ensuring the data is in suitable format for model training. The processed traffic data is then intentionally masked, and input into the traffic encoder, which employs the principles of MAE by aiming recover the original data from its corrupted form. The encoder learns to extract robust feature representations by focusing on the structure left intact by the masking process. Finally, the encoded features passed through decoder that attempts to reconstruct the original input, thereby enabling the model to learn critical data characteristics effectively and allowing the encoder extract effective traffic semantics.
Targer
Traffic
X
Encoder
Decader
011000
Traffic Pcup
Flow Content
Resized Content
Masked
Feature
For
Vector
Noise Detector
Figure 5: The pre-training paradigm of masked autoencoder for building traffic semantics-oriented encoder.
B Two Categories of the Predicted Labels from the Traffic Label Predictor
Based on the label predictor fine-tuned on the clean flows, could obtain the predicted labels of detected noisy flows. As shown in Figure we refine the labels of noisy flows into two categories, i.., high-confidence flow labels and low-confidence flow labels. The samples with high-confidence labels share a similar distribution with the clean set, and we use the prediction results to correct their labels. The samples with low-confidence flow labels represent difficult instances for the predictor. Thus, we retain their original labels and introduce them to the refined flows to maintain diversity and expand the semantic distribution of samples.
High-Confikknee Flow Lahels
Low-Coniddene Flow Labels
Confidence
Label
Predictor
Clase
Class 'y
Class
Chas
Class
Chass
Chass
Class
Class
Chass
Class 4
Chace S
Class i
Class
Figure 6: Two categories of the predicted labels from the traffic label predictor.
C
The Structure Detail of the Cross-Granularity Robust Classifier
presents the structure of the Cross-Granularity Robust Classifier, which is designed to classify network traffic at both the packet and flow levels. In the flow-level traffic classifier, multiple parallel packet encoders process each individual packet from the flow. These encoders share weights,
Figure 7
14

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indicating that they operate under a unified framework to maintain consistency in feature extraction across different packets. The encoded packet features are then aggregated to form comprehensive representation of the flow, which subsequently used to perform the flow-level traffic classification for the entire flow. This structure of parallel packet encoders the flow- level classifier enables
Flow-Jevel Traffic
Packets
Packet Engader
Encoder
9
Flow
Flow-level Classification
Packet
Random
Eneader
weight individ- ual packet encoder to process packet-level input and thus perform packet-level classification. In detail, a packet is selected randomly from the flow sample, and then classified by the packet- level traffic classifier. This dual approach allows the system to leverage fine-grained packet-level data along with aggregated flow-level informa- tion, enhancing the robustness and accuracy of the traffic classification.
Shaan Weight
Packet
Encoder
Packet
Packet-level Classification
Packet-level Traffic Classifier
Figure 7: The structure detail of the cross- granularity robust classifier.
D Dataset Information
The details of the four real-world traffic datasets used in this work are shown follows:
ISCXVPN 45 contains 7 categories of encrypted traffic, collected by the Canadian Institute for Cybersecurity via Open VPN. The dataset consists of 2,275 training samples and 569 test samples.
CrossPlatform (46) includes encrypted traffic data from various platforms. In our experiments, we use IOS application traffic in the China region, comprising 30 different categories. The dataset consists of 5,429 training samples and 1, 1,372 test samples.
USTC-TFC (16) is a traffic dataset with 10 categories of benign application traffic and 10 categories of malware traffic. The dataset consists of 1,914 training samples and 483 samples.
Malware (47) is recently published dataset featuring traffic from 10 malware families, available at htttps://malaat traffic- analiiisnaysssss peet/abut... html training samples and 740 test samples.
The dataset consists 2,938
E
Baselines
To evaluate the performance of our method, we use six state-of-the-art traffic classification methods and five label noise learning methods baselines. The following traffic classification baselines include one traditional machine learning method, three traditional deep learning methods, and three advanced pre- pre-training methods.
Appscanner 7 A traditional machine learning tool that classifies traffic using handerafted flow-based features, often less resilient to noisy encrypted data.
FS-Net (14): A flow-based model incorporating both classification and reconstruction tasks, enhancing its robustness noise and improving semantic feature extraction.
ET-BERT 32) A BERT-like Transformer pre-trained model, leveraging self-supervised learning capture flow-level semantic patterns, offering strong resilience to label noise.
MAE 34): A self-supervised traffic classifier based Masked Autoencoder (MAE), masking
parts of traffic flows and reconstructing them, which makes it robust in noisy conditions.
YaTC 35): hybrid traffic classification model combining supervised and self-superviied learning, balancing noise handling and effective traffic feature extraction.
MCRe 24): state-of-the-ar traffic analysis method focuses on malicious traffic label noise learning.
In addition, we introduce the following label noise learning baselines, all of which use the same encoder structure for fair comparison.
15

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CrossPlatform
USTC-TFC
Malware
100
100
100
100
Fl (%)
1coor (%6
Fl Store (%)
60
60
60
60
20
Deeqgeckit
20
20-
20
20
60
10
20
40
3.00
40
60
20
00
Noise Ratio (36)
Noise Ratio (%)
Noise Ratio (%)
Noise Ratoo (96)
Figure 8: Comparison of packet classification performance with packet-level baselines.
CE: A standard cross-entropy loss used in classification tasks, which can be sensitive label noise as it directly penalizes incorrect predictions.
LSR (49); Label Smoothing Regularization reduces model overconfidence by distributing probabil- ity mass to incorrect labels, making the model more robust to noise.
Mixup 4 A data augmentation technique that generates synthetic samples by interpolating
between two samples and their labels, helping the model generalize better under noisy conditions.
GCE (27): Generalized Cross-Entrop: blends the advantages of MAE and CE to address label noise, making it more robust by adjusting the loss function dynamically.
SCE (28: Symmetric Cross-Eiie Pross-Entropy introduces correction term balance the standard CE loss, aiming to better handle noisy labels by mitigating over wer-penalization of incorrect predictions.
Co-teaching (6)): Trains two networks simultaneously and lets them teach each other by selecting small-loss samples, assuming that clean samples have lower loss values. This helps filter out noisy labels during training.
DivideMix (25): Uses a Gaussian mixture model divide samples into clean and noisy sets based on loss values, then applies semi-supervised learning to train the model using both labeled and unlabeled data.
F Hyperparameter Information
In all experiments, we use batch size of 64, train for 20 epochs, and optimize with AdamW (50) using a learning rate of 4 x 10-3 For noise detection, we set the clustering granularity to 2 most frequent labels in each cluster considered majority labels.
5, with the top Confidence thresholds are set at T = 0.9 and π = 0.7 to balance precision and recall in noise filtering. Importantly, we apply the same hyperparameter settings across all datasets, demonstrating that our method achieves strong performance without extensive tuning, highlighting its robustness to hyperparameter choices.
Table 5: Hyperparameter settings in our experiments.
Parameter
Setting
Batch size
64
Epochs
20
Learning rate
x 10 3
Optimizer
Adam W (50)
Clustering granularity (n)
5
Majority label count (m)
2
High confidence threshold (7%)
0.9
Low confidence threshold (7)
0.7
G Packet Classification Ability
The proposed gross-granularity robust classifier integrates both flow-level and packet-level traffic classification tasks against label noise, bringing the ability to serve as a robust packet classifier. compare the packet-level classification performance with advanced packet classifiers, including Deeppacket (48), CNN (16), and ET-BERT (32). As shown in Figure g in packet-level traffic analysis
16

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Table 6: Comparison with Traffic Classification Baselines Class-dependent Noise Scenarios.
Dataset
Noise
Appscanner
FS-Net
ET-BERT
MAE
YaTC
MCRe
Ours
Acc
F1
Acc
F1
Acc
F1
Acc
Acc
F1
Acc
F1
Acc
F1
5%
81.49
82.07
87.42
87.58
85,76
85.77
91.39 39
91.36
93.15
92.98
80.86
81.72
93.32
92.98
10%
81.16
81.70
85.78
86.21
81.90
81.69
90.51
90.45
91.21
91.13
78.52
79.29
92.62
92.28
ISCXVPN
20%
80.51
80,36
68.15
83.04
70.29
83.33
80.49
55.18
80.52
84.35
68.71
84,44
69.50
88.75
67.31
58.64
77.54
74.61
77.38
74.75
89.45
77.29
88.90
60%
49.67
51.14
47.10
47.95
46.04
48.23
46.74
48.35
42.00
44.03
45.51
46.29
52.72
53.38
5%
62.15
94,20
94
95.24
94,12
94.50
10%
61.38
61.38
85.78
85 98
92.96
93.07
93.79
93.75
93.17
93.18
90.23
92.40
94.20
94.16
USTC-TTC
20%
60.86
55.61
80.39 39
79.97
91.51
88,62
90.68
90.65
89.45
80
92.13
92.15
40%
54.98
50.17
76.56
77.01
75.98
76.41
78.88
88.88
79.99
83.43
83.73
82.03
83.87
84,47
84,90
60%
42.19
41.75
62.34
62.52
54.65
57.11
56.10
57.27
66.87
68.28
35,55
40.02
75.36
76.30
Table 7: Comparison with Label Noise Learning Baselines on Class-dependent Noise Scenarios.
Damnet
Noise
CE
LSR
Mixup
GCE
SCE
Co-teuching
DividMix
Ours
Acc
F1
Ace
P1
Ace
F1
Acc
F1
Aec
F1
Acc
F1
Ace
F1
Ace
5%
78.91
79.06
79.61
79.66
82.95
82.74
76.09
74.55
79.09
79.06
85.41
84.44
85.24
55.33
93.32
92.98
10%
76.09
75.31
77.85
77. 77.34
78.91
78.76
75.04
73.88
77.50
76.47
86.46
85.74
80.84
81.37
92.62
92.28
ISCXVPN
76.44
76,80
76.67
74.34
74.77
79.61
80.15
89.45
58.52
59.91
67.18
76.63
77.00
78.03
60%
38.84
40.05
34.62
36.01
43.76
45.73
42.58
42.03
41.82
44.02
51.85
30.94
50.44
53:44
53.25
53.62
598
87.16
86.73
86.33
85.67
90.68
90.45
80.12
78.79
86.25
86.45
76,81
73.84
85.92
85.72
94.62
94.50
10%
85.92
86.06
81.85
83.31
80:23
85.51
79.50
78.66
85.92
85.86
77.64
74.63
90. 00.48
90.41
94.20
94.16
USTC-TRC
30%
81.78
81.27
82.81
82.27
83.22
K2.49
78.67
76.84
80.12
90
83.44
80.90
89.
98
92.13
92.15
40%
65.21
65.18
66.01
66.20
74.12
74.41
71,42
70.16
71.22
70.62
82.40
81.75
81.78
25
84.47
$4.90
58.17
65.
tasks, where each sample contains less information, the classification methods suffer from label noise more. Only FLOWREFINER can achieve FI scores higher than 80% under 20% noise ratio, while other packet-level baselines can hardly achieve 60%. demonstrates that FI.OW could obtain two levels of robust encrypted traffic classifier at once and adapt to different requirements.
H
Class-dependent Noise Evaluation
Class-dependent noise occurs when the probability of label corruption is not uniform across classes, meaning certain classes are more prone to having mislabeled samples than others.
For instance, in VPN traffic categories, VOIP and Streaming are often mislabeled due to their similar real-time transmission metrics, such as packet sizes and intervals, once encrypted. FTP and MAIL, which exhibit prolonged, high-volume traffic traits during large file transfers, are frequently miscategorized as p2p. Likewise, BROWSING and CHAT, with their frequent interactions and small packet sizes, become indistinguishable when encrypted, leading to frequent mislabeling. Thus we injected class-dependent noise into the ISCXVPN dataset to mirror these common mislabeling scenarios.
On the other hand, in the USTC-TFC malware traffic dataset, FTP and SMB often get confused due to their similar file transfer behaviors. Both protocols involve significant data packet exchanges which can appear alike under traffic analysis. Similarly, Gmail and Outlook, both being email services, often exhibit interchangeable traffic patterns due to similar data flow structures, leading to potential mislabeling. Services like Skype and Facetime, which both facilitate VoIP communications, show closely related network signatures that can easily be mistaken for one another when encrypted. Additionally, applications like WorldOOWareraft and Skype may be misclassified due to their real-time interaction requirements, which create similar traffic spikes. BitTorrent, known for peer-to-pen -eer file sharing, and FTP, used for direct file transfers, also share large file movement characteristics that can be confused under automated analysis. Furthermore, malware such as Zeus and Cridex, or Nsis-ay and Virut, exhibit overlapping behaviors in terms of their network communication patterns, making accurate classification challenging. Thus, we injected these class-dependent noises into the UST dataset closely simulate these realistic mislabeling scenarios.
We comprehensively performed comparison with both traffic classification methods and label noise methods under class-dependent noise, which are shown in Table and Table Results show that our method can achieve significant performance advantages over the baselines in the class-dependent noise scenario.
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Reviews:
[{"review_id": "A7c3A6OnWs", "reviewer": "Reviewer_72E3", "summary": "The paper presents FlowRefiner, a novel framework for traffic classification in the presence of label noise. It introduces a three modular comprising a noise detector, a label corrector, and a cross-granularity classifier. The proposed method is thoroughly evaluated on four datasets under varying levels of label noise, consistently outperforming state-of-the-art baselines.", "questions": "1.\tWhat exactly does a dataset sample refer to a flow or packet?\n\n2.\tHow is label noise introduced in the experiments? Is it added by randomly selecting samples or based on a specific pattern?\n\n3.\tCan the authors provide a more detailed qualitative analysis of failure cases, such as confusion between specific classes?\n\n4.\tHow well does it generalize across different domains? Does the encoder require dataset-specific fine-tuning or post-tuning, especially as traffic characteristics evolve over time?\n\n5.\tHow do the authors justify the scalability of the framework to real-world deployment?\n\n6.\tWhen stating that \"the framework takes 3 minutes on a GPU,\" does this refer to training time, inference time, or the full pipeline duration?", "limitations": "The authors are upfront about the framework’s computational requirements, which may pose scalability challenges as mentioned above. I suggest providing a more detailed analysis, including separate reporting of training and inference times. For future work, the authors could explore various acceleration techniques such as model distillation and pruning to improve efficiency.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths: \n1.\tThe paper presents a well-designed framework for traffic classification under noisy labels, addressing an important and impactful problem in the community. \n2.\tThe design of framework is well-motivated and supported by comprehensive experiments. \n3.\tThe paper is clearly written and well-organized, making it easy for readers to follow. \n\n\n\nWeaknesses: \nRegarding experiment setups and result analysis:\n1.\tThe definition of a dataset sample is unclear — does it refer to flows or packets?\n2.\tAdditionally, the process for introducing label noise is not well explained. Is the noise added by randomly selecting samples or by following a specific pattern?\n3.\tWhile real-world label correction accuracy is reported, a more detailed qualitative analysis of failure cases (e.g., confusion between specific classes) would help strengthen the claims.\n\nRegarding generalizability:\n1. The performance of the framework appears to rely heavily on the pre-trained encoder, whose generalizability across different domains remains unclear. It is also not specified whether the encoder requires dataset-specific fine-tuning or post-tuning, especially as traffic characteristics may evolve over time.\n\nRegarding scalability:\n4.\tThe dataset scale appears limited, with only a few thousand samples per dataset. The authors should clarify how such experiments support claims of real-world scalability.\n5.\tThe reported runtime—\"the framework takes 3 minutes on a GPU\"—is ambiguous. It is unclear whether this refers to training time, inference time, or the entire pipeline.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your time and careful review of our work! \nWe hope our responses below address your concerns.\n\n**Sample Definition.** \n> **Q1**: The definition of a dataset sample is unclear — does it refer to flows or packets?\n\nEach dataset sample in our experiments corresponds to a network flow, which is defined by the standard 5-tuple (source IP, destination IP, source port, destination port, and protocol). This is consistent with common practice in network traffic classification literature (e.g., [30, 33]). \n\n\nAdditionally, in our Cross-Granularity Robust Classifier, each packet is sampled from an individual corresponding flow sample to perform pcaket-level classification task, which is detailed in Section 3.3.\nWe will clarify these definitions in the revision for better reproducibility.\n\n\n\n\n**Noise Introducing Process.** \n\n> **Q2**: How is label noise introduced in the experiments? Is it added by randomly selecting samples or based on a specific pattern?\n\nWe introduce label noise following established protocols in label noise learning, e.g., [22][23][24] in our manuscript. \nOur experiments involve symmetric noise, which is added by randomly selecting a specified percentage of samples (e.g., 20%, 40%, 60%) and uniformly flipping their labels to other classes.\n\nFurthermore, we consider class-dependent noise based on traffic similarity patterns for evaluation in Section 4.2 and Appendix H. As detailed in Appendix H, we introduce class-dependent scenarios by assigning higher mislabeling probabilities between semantically similar classes of traffic.\n\n\n\n\n\n\n\n**Failure Cases.**\n\n> **Q3**: Can the authors provide a more detailed qualitative analysis of failure cases, such as confusion between specific classes?\n\n\nThank you for your suggestion. We present a more detailed qualitative analysis of failure cases using the confusion matrices of ISCXVPN under different noise rates.\n\nAt a 20% noise rate, the most significant off-diagonal errors are observed between CHAT and VOIP, FTP and VOIP, and BROWSING and Streaming. These errors typically occur between semantically similar classes. For instance, both CHAT and VOIP are real-time communication applications with overlapping traffic patterns, making them challenging to distinguish, especially under label noise.\n\nAs the noise rate increases to 40% and 60%, we observe an increase in confusion among these related classes, particularly among CHAT, VOIP, and FTP. Despite the higher label noise, the main diagonal entries remain dominant, demonstrating the robustness of our method against label noise.\n\nWe will include the confusion matrix in the revision.\n\n\n\n**About Generalizability.**\n> **Q4**: How well does it generalize across different domains? Does the encoder require dataset-specific fine-tuning or post-tuning, especially as traffic characteristics evolve over time?\n\nIn our framework, the pre-trained encoder used in the noise detector is frozen and does not require any dataset-specific fine-tuning or post-tuning. \nAs stated and proved in recent advances (e.g., Ref [30, 32, 33]), well-designed pre-trained encoders can generalize across diverse traffic domains and downstream tasks.\nOur results on multiple datasets, including the real-world CICIDS dataset, further support this generalizability. \n\nWe agree that as network traffic characteristics evolve, continual adaptation is an important direction, and we plan to explore online adaptation and domain adaptation techniques in future work.\n\n\n\n\n\n**About Scalability.**\n\n> **Q5**: How do the authors justify the scalability of the framework to real-world deployment?\n\nOur evaluation follows the classic datasets commonly used in the field of traffic analysis [30][32][33], which contain only thousands of flows.\nHowever, to demonstrate real-world robustness and scalability, we conduct experiments (Section 4.3) on the CICIDS2017 dataset, which contains over 200,000 flows. \nOur framework maintains robust performance on this large-scale dataset, indicating its practical applicability for real-world deployment. We will highlight these large-scale results more clearly in the revised manuscript.\n\n\n\n\n> **Q6**: When stating that \"the framework takes 3 minutes on a GPU,\" does this refer to training time, inference time, or the full pipeline duration\n\nThe “3 minutes on a GPU” refers to the average total runtime (176.28 seconds) of the full pipeline (including noise detector, label correction, and robust classification) across all four benchmark datasets, measured on a single NVIDIA RTX 3090 GPU.\nFor clarity, we also provide the detailed runtime for each component on all datasets in the table below.\n\n| Time (s)              | ISCXVPN  | CrossPlat | USTC     | Malware  |\n| --------------------- | -------- | --------- | -------- | -------- |\n| Noise Detector        | 7.2895   | 16.9373   | 6.4972   | 8.2633   |\n| Label Corection       | 39.6972  | 63.5462   | 34.8589  | 45.1194  |\n| Robust Classification | 99.7816  | 175.7102  | 90.3483  | 117.0476 |\n| Total Pipeline        | 146.7719 | 256.1984  | 131.7097 | 170.4369 |"}, {"review_id": "4Y1kNcxKYO", "reviewer": "Reviewer_MEnh", "summary": "The authors proposed FLOWREFINER framework to solve noise-label related network classification problems. \nWith a three-step workflow, FLOWREFINER can detect outlier labels by clustering, correct noise based on predictor confidence and implement final classification with a cross-granularity classifier.\nIn experiments, authors compared FLOWREFINER with different network classification methods and label noise learning methods under manully noise added datasets. Results show FLOWREFINER outperform other methods among all noise related scenarios.\nFinally authors discussed several aspects that may influence the noise learning performances and future plan of their framework.", "questions": "1. Cross-platform dataset consists servral regions and two different operation systems, which make it naturally suitable for better noise setting (for example, India dataset has systematic noise from the perspective of China region data). Can such data setting be used in experiments to furtherly demonstrate the novelty of FLOWREFINER?\n2. Authors introduced the influence of different settings for cluster number parameter n, majority label count m. However, it will be better to demonstrate the selection basis of  noise correlation confidences.\n3. Network traffic can be processed as sequence or images. Author considerd the traffic as images for MAE pretraining, will there be any difference when we consider such data as sequences like what happened in ET-BERT?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The motivation and design process of the FLOWREFINER framework is clear and consistent.\n2. Detailed designs for each component is practical for solving targted tasks and related experiments are abundant.\n3. The writing is clear and easy to ubderstand.\n\nWeakness:\n1. The cluster number parameter n, majority label count m and confidence thresholds rely on actual experiment datasets, which might affect the generalization among different scenarios.\n2. All datasets' noise come from manually twisting instead of  naturally deterioration by time or locations, which makes the noise easier to detect.", "was_revised": true, "final_justification": "During the rebuttal period, authors provides additional experimental results, especially the one regarding distribution shift, which addresses my concerns. I have updated my score.", "rebuttal": "We sincerely appreciate your time and the positive feedback!\nWe hope our responses below further clarify our work and address your concerns.\n\n\n\n\n**About Hyperparameters.**\n> W1: ... thresholds rely on actual experiment datasets, which might affect the generalization among different scenarios.\n\nWe are happy to clarify that we apply the same hyperparameter settings across all datasets in evaluation. The consistent robustness demonstrates that our method achieves strong performance without extensive tuning, highlighting its generalizability among different scenarios.\n\nIn addition, our hyperparameter design also incorporates moderate adaptivity.\nSpecifically, the clustering granularity parameter is used to scale with the number of classes in each dataset, and together they determine the final cluster count.\nThis allows the clustering process to better adapt to varying dataset characteristics, further reducing the need for manual adjustment.\n\n\n**About Manual Noise.**\n\n> W2: All datasets' noise come from manually twisting instead of naturally deterioration by time or locations, which makes the noise easier to detect.\n\nWe follow widely-adopted practices of symmetric manual noise in label-noise literature in our evaluation. \nThe results show that current methods of traffic classification or label noise learning can hardly achieve good performance under such settings.\n\nAdditionally, we evaluated our method on the real-world noisy dataset CICIDS2017 in Sec 4.3, which naturally contains noisy labels according to previous studies [18,19]. \nAs shown in Figure 3 (c), our method successfully identifies 84.61% of the noisy flows under real-world noise, and the accuracy of our noise detection is 81.92%.\nThe results demonstrate the effectiveness of our method under real-world noise.\n\n\n**Response to Questions.**\n\n> **Q1**: \"Cross-platform dataset consists servral regions and two different operation systems, which make it naturally suitable for better noise setting (for example, India dataset has systematic noise from the perspective of China region data). Can such data setting be used in experiments to furtherly demonstrate the novelty of FLOWREFINER?\"\n\nThank you for the insightful suggestion. However, the described cross-platform dataset scenario primarily reflects domain shifts rather than actual label noise, i.e., mislabeling within the same categories. \nSince the India and China datasets have different application categories, which are collected according to regional popularity, they cannot directly serve to evaluate under label noise scenarios. We will release our codes and further evaluate FlowRefiner on more scenarios in future work.\n\n\n\n\n> **Q2**: Authors introduced the influence of different settings for cluster number parameter n, majority label count m. However, it will be better to demonstrate the selection basis of noise correlation confidences.\n\nWe are happy to provide the detailed discussions about confidence thresholds. The influence to F1 scores of these thresholds on ISCXVPN dataset under different noise ratios is shown as follows.\n\n\n| High-confidence Thresholds | 20%       | 40%       | 60%       |\n| :------------------------- | :-------- | :-------- | :-------- |\n| 0.95                       | 88.25     | 81.32     | 69.92     |\n| 0.90                       | **90.15** | **84.19** | **72.90** |\n| 0.80                       | 86.94     | 81.78     | 72.49     |\n| 0.70                       | 84.03     | 80.68     | 70.44     |\n\n\n\n| Low-confidence Thresholds | 20%       | 40%       | 60%       |\n| ------------------------- | :-------- | :-------- | :-------- |\n| 0.80                      | 87.03     | 83.29     | 70.63     |\n| 0.70                      | **90.15** | **84.19** | **72.90** |\n| 0.60                      | 86.42     | 83.54     | 72.68     |\n| 0.50                      | 84.52     | 82.80     | 72.84     |\n\n\n\n\n\n- The optimal setting for the high confidence threshold is 0.90, which yields the best performance across all noise ratios. When the threshold is set too high (i.e., 0.95), the model becomes overly cautious, failing to relabel flows that could have been corrected, leading to a decline in performance. On the other hand, setting the threshold too low (i.e., 0.70-0.80) can lead to overcorrection by relabeling flows that are uncertain, thereby introducing more potential noise into the raw flows, especially under the high noise ratio. \n- For the low confidence threshold, the optimal setting is 0.70. This setting ensures that the flows outside the clean set’s distribution are reintroduced with their original labels, preserving the diversity of the dataset without overwhelming it with potential noise.\n\n\n\n\n> **Q3**: Network traffic can be processed as sequence or images. Author considerd the traffic as images for MAE pretraining, will there be any difference when we consider such data as sequences like what happened in ET-BERT?\n\n\nWe process the traffic into sequences and replace the encoder of FlowRefiner with ET-BERT. The results are shown below:\n\n\n\n| Dataset   | Method       | 20%       | 40%       | 60%       |\n| --------- | ------------ | --------- | --------- | --------- |\n| ISCXVPN   | ET-BERT      | 79.07     | 61.78     | 36.21     |\n| ISCXVPN   | ET-BERT+Ours | **84.69** | **75.82** | **60.58** |\n| CrossPlat | ET-BERT      | 94.49     | 79.30     | 59.40     |\n| CrossPlat | ET-BERT+Ours | **98.68** | **94.15** | **71.87** |\n| USTC      | ET-BERT      | 92.34     | 81.69     | 58.09     |\n| USTC      | ET-BERT+Ours | **95.86** | **91.42** | **79.93** |\n| Malware   | ET-BERT      | 78.03     | 61.49     | 43.38     |\n| Malware   | ET-BERT+Ours | **78.08** | **75.44** | **65.33** |\n\nThese results indicate that FlowRefiner maintains strong robustness and significantly outperforms original ET-BERT across datasets and noise levels. Since sequence-based language models typically require significantly greater computational resources than image-based models, FlowRefiner processes traffic as images to remain more efficient and practical in real-world deployments."}, {"review_id": "uXWZUQZXdz", "reviewer": "Reviewer_HgNa", "summary": "This paper presents FlowRefiner, a robust and general-purpose framework for network traffic classification that addresses label noise. The framework uses three core components to tackle label noise issues. Experiments on four real-world traffic datasets show that FlowRefiner significantly outperforms existing baselines across different noise ratios.", "questions": "1. Have other pre-training methods been considered for semantic extraction? How does it generalize to complex scenarios like encrypted traffic?\n2. How are loss functions balanced between flow-level and packet-level tasks? Is there a risk of performance degradation due to task conflicts? Can you provide more details on parameter sharing strategies?\n3. Which network devices can FlowRefiner be deployed on? Can existing network devices’ computing resources support deployment, given that traffic classification often requires line-rate processing in many scenarios?\n4. Experiments assume uniform noise distribution, but real networks may have localized high noise. How robust is FlowRefiner against non-uniform noise distributions?", "limitations": "1. Performance of the noise detection module is constrained by the pre-trained encoder’s semantic extraction capability. If pre-training data differs significantly from the target domain, re-pretraining is needed.\n2. FlowRefiner requires multiple data traversals and model updates, making edge device deployment difficult.\n3. It primarily targets random label noise. While its effect on class-dependent noise is evaluated, robustness in extreme class-dependent scenarios needs enhancement.\n\nIf the authors can solve the above problems, I would be happy to raise the score.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The paper is well-structured with precise and professional writing.\n2. It introduces a label noise handling framework for general traffic classification, breaking through the limitations of existing methods that focus on specific scenarios like malicious traffic.\n3. It uses four datasets from different scenarios, covering noise ratios from 5% to 60%, and compares against comprehensive baselines including traditional methods, pre-trained models, and label noise-specific approaches.\n\nWeaknesses:\n\n1. The pre-trained encoders rely on existing techniques, with innovation focused on framework design rather than underlying representation learning.\n2. Key parameters like clustering granularity and majority label counts require manual setting, with no mention of adaptive adjustment mechanisms, which may limit generalization to unknown datasets.\n3. Random sampling in packet-level classification may lose critical packet sequence information, and the framework’s adaptability to time-sensitive traffic is not discussed.\n4. Computational overhead is not reported, and robustness in real-world scenarios needs further validation.", "was_revised": true, "final_justification": "Given authors' comprehensive response, I think this work still has some significance, so I raise my score to 5.", "rebuttal": "We appreciate the thoughtful review and the constructive feedback! We hope the following clarifications and experiments address your concerns.\n\n**About Pre-trained Encoder.**\n\n> W1: The pre-trained encoders rely on existing techniques, with innovation focused on framework design rather than underlying representation learning.\n \nWe clarify that the pre-trained encoder extracts the semantics of the traffic from the raw data for FlowRefiner, which is first proposed as the unique insight for effectively detecting outlier labels as noise.\n\nBesides, the point of our innovation is not in traffic representation learning. As stated in the introduction, our core contribution is the first robust framework to address the critical label noise issue in general traffic classification, a problem that is not handled by previous works.\n\n\n**About Parameters.**\n\n> W2: Key parameters like clustering granularity and majority label counts require manual setting, with no mention of adaptive adjustment mechanisms, which may limit generalization to unknown datasets.\n\n\nAs stated in Appendix F, we use the same parameter settings across all datasets, demonstrating that our method achieves strong performance without extensive tuning.\nIn addition, our parameter design incorporates moderate adaptivity. \nSpecifically, the clustering granularity is used to scale the number of dataset classes, and together they determine the final cluster count.\nThus, the clustering can better adapt to different datasets, reducing the need for manual adjustment.\n\nMoreover, we are happy to further explore more automated hyperparameter tuning methods in future work.\n\n**About Random Sampling.**\n\n> W3: Random sampling in packet-level classification may lose critical packet sequence information\n\nWe utilize random sampling to obtain varied packet samples from the same flow during training, reducing model overfitting to single samples under label noise.\nOn the other hand, the critical packet sequence information is captured by the flow-level task with the same share-weighted classifier.\nThis design enables our model to leverage both diverse local packet information and global flow-level context, achieving robust performance under label noise.\n\n**Time-sensitive Adaptability.**\n\n> W3: the framework’s adaptability to time-sensitive traffic is not discussed.\n\n\nOur two-level robust classifier addresses this need, providing both flow-level and packet-level heads and thus supporting different deployment requirements (e.g., time-sensitive traffic).\nIn time-sensitive scenarios, the packet-level classification could be performed to avoid waiting for entire flows.\n\nThe packet classification ability is detailed in Appendix G. Under a 20% noise ratio, FlowRefiner achieves F1 scores above 80% on all datasets, while other packet-level baselines struggle to reach 60%. This demonstrates that our method can perform accurate and robust packet-level classification, making it suitable for time-sensitive traffic scenarios.\n\n\n**Computational Overhead.**\n\n> W4: Computational overhead is not reported.\n\nWe reported the time and computational overhead in Limitations of Section 4.5. As stated, our framework currently takes 3 minutes on an RTX 3090 GPU with 2.79 GB of memory.\n\n\n> L2: FlowRefiner requires multiple data traversals and model updates, making edge device deployment difficult.\n\nNote that 'multiple data traversals and model updates' are performed during the training stage. \nIn the testing (deployment) stage, FlowRefiner operates identically to existing deep learning traffic methods, introducing no additional procedures or overhead at inference time.\n\n\n**Response to Questions.**\n\n> **Q1**: Have other pre-training methods been considered for semantic extraction? How does it generalize to complex scenarios like encrypted traffic?\n\nWe further considered ET-BERT, an encrypted traffic pre-training method, as the encoder of our framework.\nAnd the evaluation on two classic encrypted traffic datasets is as follows:\n\n| Dataset   | Method       | 20%       | 40%       | 60%       |\n| --------- | ------------ | --------- | --------- | --------- |\n| ISCXVPN   | ET-BERT      | 79.07     | 61.78     | 36.21     |\n| ISCXVPN   | ET-BERT+Ours | **84.69** | **75.82** | **60.58** |\n| CrossPlat | ET-BERT      | 94.49     | 79.30     | 59.40     |\n| CrossPlat | ET-BERT+Ours | **98.68** | **94.15** | **71.87** |\n\nThese results demonstrate that our framework consistently improves robustness over the base ET-BERT, especially under high noise ratios, and generalizes well to encrypted traffic scenarios.\n\n\n> **Q2.1**: How are loss functions balanced between flow-level and packet-level tasks? \n\nWe do not introduce additional hyperparameters to balance the losses of the flow-level and packet-level tasks.\nConcretely, in each training epoch, we first perform flow-level classification and backpropagate its loss, and then conduct the packet-level task using the same encoder weights. \nThis alternating process avoids manual loss weighting and lets the shared encoder learn features beneficial to both tasks.\n\n> **Q2.2**: Is there a risk of performance degradation due to task conflicts? \n\nWe are happy to clarify that flow- and packet-level tasks complement each other, rather than causing conflicts.\nAs demonstrated in Table 4 of Section 4.4, we remove the packet-level classification branch (i.e., w/o cross-granularity robust classifier), which leads to lower performance across all datasets and noise ratios.\nThis result highlights that the joint training of flow-level and packet-level tasks is mutually beneficial, without risk of performance degradation.\n\n> **Q2.3**: Can you provide more details on parameter sharing strategies?\n\nWe detailed the parameter sharing strategies in Appendix C, Figure 7.\nIn the flow-level classifier, multiple parallel packet encoders process each individual packet from the flow; these encoders share the same weights. The encoded packet features are then aggregated to form the flow representation.\nThis design enables the same parameter encoder could also handle packet data and perform packet-level classification.\n\n> **Q3**: Which network devices can FlowRefiner be deployed on? Can existing network devices’ computing resources support deployment, given that traffic classification often requires line-rate processing in many scenarios?\n\n\n\n\n\nTo further address efficiency and deployment concerns, we performed additional experiments under a variety of hardware settings, including GPU, CPU, and an edge-device simulation:\n\n\n|                | GPU           | CPU          | Edge Device |\n| -------------- | ----------------- | ---------------- | -------------------------- |\n| Inference Time | 0.24 ms          | 5.31 ms        | 145.07 ms                 |\n| Throughput     | 4226.88 samples/s | 188.26 samples/s | 6.89 samples/s             |\n\nThe edge device is simulated via a single-core CPU, 50% CPU quota, 1GB memory with swap disabled, and instruction set limited to AVX2.\nThese results show that FlowRefiner can be flexibly deployed on a range of network devices, from high-performance GPUs to constrained edge devices.\n\n\n> **Q4**: Experiments assume uniform noise distribution, but real networks may have localized high noise. How robust is FlowRefiner against non-uniform noise distributions?\n\nWe introduce non-uniform (class-dependent) noise evaluation in lines 299-308 of Section 4.2.\nThe details are shown in Appendix H, Table 6, Table 7.\nResults show that our method can achieve significant performance advantages over the baselines against non-uniform noise distributions.\n\nAdditionally, we also evaluate FlowRefiner under real-world non-uniform noise of CICIDS2017 in line 324-331, Section 4.3.\nThe results demonstrate the ability of our method to handle real-world non-uniform noise.\n\n**About Limitations.**\n\nWe appreciate your valuable suggestions. \nFuture work will explore domain-adaptive pre-training techniques and more extreme class-dependent noise scenarios to enhance robustness further."}, {"review_id": "va54dXaTHs", "reviewer": "Reviewer_fb8o", "summary": "This paper proposes FlowRefiner, a three-stage framework for network traffic classification in the presence of label noise. The framework first uses a \"traffic semantics-driven noise detector,\" which leverages a pre-trained Transformer-based encoder to generate embeddings and then applies clustering to identify noisy samples. Second, a \"confidence-guided label correction\" module refines the labels of suspected noisy flows. Finally, a \"cross-granularity robust classifier\" is trained on the cleaned data, using both flow-level and packet-level information to improve robustness. The authors conduct an extensive experimental evaluation on four datasets, demonstrating that their method significantly outperforms several existing baselines under various types of synthetic and real-world label noise.", "questions": "1. Did you use a session-aware or time-based splitting strategy to avoid leakage? If not, could you re-evaluate your results with a more robust approach?\n2. Can you provide stronger theoretical or empirical justification for the choice that keeps the low-confidence samples? Have you compared this against simply removing or correcting such samples?\n3. If possible, could you please add related methods like RAPIER and ULDC as baselines?\n4. Could you more clearly distinguish your contributions from existing literature? What specific insights or components in FlowRefiner are novel beyond architectural substitutions?", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The paper is well-structured, clearly written, and addresses a practical and important problem in the domain of network security and management: the prevalence and impact of label noise on traffic classification.\n\n### **Strengths**\n1. **Clarity and Quality of Presentation:** The paper is exceptionally well-written. The proposed FlowRefiner framework is described in a clear, step-by-step manner, and the figures are informative. The overall presentation quality is high.\n2. **Thorough Experimental Evaluation:** The authors have conducted a comprehensive set of experiments across multiple datasets, various noise types (symmetric, asymmetric, class-dependent), and high noise ratios. The ablation study effectively demonstrates the contribution of each component of the framework. The performance gains shown over the chosen baselines are indeed substantial.\n3. **Problem Significance (for its domain):** Label noise is a genuine challenge for applying deep learning to real-world network traffic data. A robust framework to mitigate this issue is a valuable contribution to the applied networking and security community.\n\n### **Weaknesses**\n1. **Lack of Originality:** The primary weakness of this paper is the overstatement of its novelty. The core technical ideas are direct applications or minor variations of well-established techniques in the label noise and traffic analysis literature:\n    - The \"traffic semantics-driven noise detector\" is conceptually identical to prior work that uses unsupervised pre-training to learn embeddings and then performs outlier detection in the feature space via clustering or density estimation. For instance, frameworks like RAPIER [1] and ULDC [2] employ a nearly identical pipeline of autoencoder-based feature extraction followed by distribution-based confidence scoring to identify noisy labels. The use of a Transformer encoder (MAE) is a modern architectural choice, but it does not change the fundamental, pre-existing concept.\n    - The idea of using cross-granularity (flow and packet level) information for robustness is also not new. Prior work has explored multi-granularity and hierarchical approaches in traffic classification with the explicit motivation of improving robustness to noise and variations [3]. While the specific implementation might differ, the core idea is not a novel contribution.\n2. **Lack of Experimental Comparison**: The paper seems to omit the most relevant baselines. The authors should have compared FlowRefiner against other end-to-end noisy label frameworks for traffic, such as the aforementioned RAPIER and ULDC.\n3. **Questionable Methodological Choices:**\n    - **Handling of Low-Confidence Samples.** The framework identifies a set of likely noisy flows, and for samples within this set where the label predictor has low confidence, the authors opt to \"retain their original labels\" in order to \"maintain diversity.\" While the intention to preserve diversity is understandable, this decision raises important concerns. Retaining samples that are likely mislabeled and reintroducing them into the training set could inadvertently compromise the effectiveness of the data cleaning process and potentially degrade the performance of the final classifier. The rationale for maintaining diversity would benefit from stronger theoretical grounding or empirical evidence to justify this trade-off.\n    - **Insufficient Detail and Potential Data Leakage.** The description of the data preparation process would benefit from additional detail, as its current form raises questions about the validity of the reported results. While the paper provides the sizes of the training and testing sets, it does not clarify how these splits were performed. This is particularly important for network traffic data, which often exhibits strong temporal and session-based dependencies. A simple random split of flows or packets could inadvertently lead to data leakage, for example, when data from the same session appears in both training and test sets, potentially leading to overestimated performance metrics. It would strengthen the paper significantly if the authors clarified whether a more robust splitting strategy (such as by time or by user/host) was employed.\n\nOverall, while the paper tackles an important problem in network security and presents a technically sound solution, its contributions are largely incremental and grounded in well-established methods. The work offers more of an engineering refinement than a fundamental advancement in machine learning. As such, it may have limited appeal to the broader NeurIPS community and would be better suited for a more specialized venue.\n\n[1] Qing, Y., Yin, Q., Deng, X., Chen, Y., Liu, Z., Sun, K., ... & Li, Q. (2023). Low-quality training data only? A robust framework for detecting encrypted malicious network traffic. arXiv preprint arXiv:2309.04798.\n\n[2] Yuan, Q., Zhu, Y., Xiong, G., Wang, Y., Yu, W., Lu, B., & Gou, G. (2024). ULDC: Unsupervised learning-based data cleaning for malicious traffic with high noise. The Computer Journal, 67(3), 976-987.\n\n[3] Tang, P., Dong, Y., Mao, S., Wei, H. L., & Jin, J. (2023). Online classification of network traffic based on granular computing. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(8), 5199-5211.", "was_revised": true, "final_justification": "My score has increased because the authors have proven the technical soundness and experimental correctness of their work. However, my recommendation is not a strong acceptance because the central question of the paper's positioning and its alignment with a fundamental methodology conference like NeurIPS remains. The work is a high-quality contribution, but its impact may be better appreciated at a top-tier applied venue.", "rebuttal": "Thank you for your constructive feedback and thoughtful suggestions. We hope that our detailed responses below will address your concerns.\n\n### **About Originality.**\n\n> W1: The core technical ideas are direct applications or minor variations of well-established techniques in the label noise and traffic analysis literature\n\n\nWe respectfully clarify that FlowRefiner is not a 'direct application or minor variation' of existing methods, but rather the **first work tackling label noise in general and complex traffic scenarios**, a challenge that has not been addressed in previous literatures.\n\n> **Q4**: Could you more clearly distinguish your contributions from existing literature? What specific insights or components in FlowRefiner are novel beyond architectural substitutions?\n\n\nBelow, we further clarify our fundamental novelty and distinct contribution compared to prior works such as [1][2][3], in terms of insights, methodology, and differences:\n\n\n### **About RAPIER [1] and ULDC [2].**\n\n> W1.1: The \"traffic semantics-driven noise detector\" is conceptually identical to prior work ... like RAPIER [1] and ULDC [2]\n\nThe mentioned RAPIER and ULDC, which **are already cited** ([21,37]) and **discussed** in our Introduction (lines 44–51) and Related Work (lines 101–104).\n\n### **Insights Beyond [1][2].**\n\n\nSpecifically, previous methods (e.g., RAPIER, ULDC) **solely rely on samples themself** (density or distance) to determine \"real label\" with **simplified assumptions**:\n - RAPIER **requires clear density differences between normal and malicious** traffic, binary relabeling samples in densest and sparsest regions accordingly.\n - ULDC defines a single center for each class and considers distant samples as noisy, which **assumes samples from different classes are obviously separable**.\n \nHowever, our core insight is that **general traffic classification tasks are inherently more complex than these simplified assumptions**. As illustrated in our manuscript (lines 156-159 and Figure 2):\n\n - **Same class** samples often exhibit **distinct** semantics, e.g., MAIL traffic covers both large attachment transfers and quick, small email exchanges with behavior diversity.\n - **Different class** samples may appear semantically **similar**, e.g., both FTP and p2p traffic transfer files and therefore produce highly similar behaviors.\n\n### **Methodology Beyond [1][2].**\n\nMotivated by this critical insight, where **real labels cannot be reliably determined by sample features alone**, FlowRefiner introduces the following key novelties:\n \n - **Sematics extraction**: we introduce pre-trained encoder to extract **high-dimentional semantics**, which can validly represent traffic behaviors. In contrast, RAPIER and ULDC use AE only to reduce the dimension of the feature for density or distance calculations.\n - **Noise detection**: we innovatively utilize **label distribution** within semantic clusters to detect label noise, going beyond previous works that identify labels merely via sample distance or density.\n - **Noise correction**: we further refine detected noisy flows according to the  **confidence of the predictor**, which is fine-tuned by clean flows to correct detected noisy samples. In comparison, the 'confidence' of ULDC is simply defined by sample distance.\n\n\n### **Differences from [3].**\n\n> W1.2: Prior work has explored multi-granularity ... with the explicit motivation of improving robustness to noise and variations [3]. \n\nWe clarify the **irrelevance** of [3] and FlowRefiner, as the **definitions of both noise and granularity** in [3] are completely **different** from ours**:\n\n\n|          | Noise Definition | Granularities |\n| - | - | - |\n| [3]         | **Network noise**: packet loss, retransmission, and disorder in dynamic networks | Spatial granule and temporal granule: **neighborhood packets** with similar packet size and interarrival time |\n| FlowRefiner | **Label noise**: incorrectly labeled samples                 | Flow-level and packet-level: two granularities of **classification tasks** in traffic analysis |\n\n\n- Noise in [3] refers to packet loss, retransmission, and disorder in dynamic networks, which is irrelevant to our label noise issues.\n- [3] defines spatial granule and temporal granule as neighborhood packets with similar packet size and interarrival time separately. In contrast, our cross-granularity describes the flow- and packet-level classifier. \n\n\n\n\nAs detailed above, we firmly believe these explicit distinctions clearly establish FlowRefiner’s fundamental novelty and significant practical contributions, distinctly differentiating it from prior works, e.g., [1],[2],[3].\n\n### **Lack of Experimental Comparison.**\n\n> W2: The paper seems to omit the most relevant baselines. The authors should have compared FlowRefiner against other end-to-end noisy label frameworks for traffic.\n\nAs stated in Section 4.1 and Appendix E, we included **MCRe**, an **end-to-end** malicious traffic label noise method, as the **most relevant baseline**. Additionally, ULDC claims itself as a data cleaning method instead of an end-to-end method.\n\n\n> **Q3**: If possible, could you please add related methods like RAPIER and ULDC as baselines?\n\n- RAPIER **cannot support multi-classification tasks methodologically**, thus it is not applicable to our general evaluation.\n- ULDC did not release their source code, but we compared **MCRe[22], which is the follow-up work of the same authors that proposed ULDC**, in Section 4. And the MCRe paper compared ULDC and achieved better performance. \n\nNevertheless, we further reproduced ULDC, and the results are as follows:\n\n\n\n| ISCXVPN | 5% | 10%       | 20%       | 40%       | 60%       |\n| - | - | - | - | - | - |\n| ULDC  | 48.57     | 40.53     | 39.84     | 39.27     | 38.36     |\n| ULDC wo noise clean | 73.09     | 72.68     | 70.88     | 69.33     | 62.60     |\n| MCRe | 83.63     | 81.63     | 80.64     | 76.62     | 70.55     |\n| Ours  | **93.34** | **90.48** | **90.15** | **84.19** | **72.90** |\n\n| USTC-TFC | 5%        | 10%       | 20%       | 40%       | 60%       |\n| - | - | - | - | - | - |\n| ULDC | 69.94   | 67.73   | 66.12     | 65.65     | 62.27     |\n| ULDC wo noise clean | 90.30     | 84.94     | 79.06     | 78.04     | 65.95     |\n| MCRe | 94.91     | 94.71     | 92.91     | 91.41     | 75.02     |\n| Ours | **96.02** | **95.60** | **93.93** | **91.69** | **78.71** |\n\n\n\nThe results demonstrate that both MCRe and FlowRefiner outperform ULDC. ULDC can not handle label noise because its simple noise cleaning assumption cannot adapt to general scenarios.\n\n\n\n### **Handling of Low-Confidence Samples.**\n\n> W3.1 and **Q2**: Can you provide stronger theoretical or empirical justification for the choice that keeps the low-confidence samples? Have you compared this against simply removing or correcting such samples?\n\nWe further provide the F1 scores under different processes of low-confidence samples:\n\n|ISCXVPN|5%|10%|20%|40%|60%|\n| - | - | - | - | - | - |\n| correcting low-confidence samples| 89.32 | 83.99 | 81.57 | 79.31 | 69.59 |\n| removing low-confidence samples  | 90.56 | 86.00 | 81.98 | 80.39 | 71.00 |\n| Ours            | **93.34** | **90.48** | **90.15** | **84.19** | **72.90** |\n\nThe results show that removing or correcting low-confidence samples consistently leads to worse performance. Since such samples are few and ambiguous, keeping their original labels has a limited negative impact but preserves diversity and boundary coverage that benefits model robustness.\n\n\n### **Insufficient Detail.**\n\n> W3.2 and **Q1**: Did you use a session-aware or time-based splitting strategy to avoid leakage? If not, could you re-evaluate your results with a more robust approach?\n\nWe perform strict session-aware splitting to avoid leakage, where each flow corresponds to one sample. Even in the evaluation of packet classification, we still ensure each packet is sampled from a unique flow. \nThis strict setting of traffic splitting avoids potential data leakage with unexpected dependencies, and ensures a fair and robust evaluation of our method’s performance.\nWe will clarify these details in the revision."}]

Author rebuttal:

Meta-review: This paper introduces FlowRefiner, a modular framework for traffic classification under noisy labels, consisting of a noise detector, a label corrector, and a cross-granularity classifier. The strengths of the work lie in its clear problem motivation, well-structured design, strong experimental validation across multiple datasets and noise ratios, and clarity of presentation. The main weaknesses are related to experimental evaluation, such as questions around noise modeling, scalability, parameter sensitivity, and robustness to more realistic deployment settings. During the rebuttal, the authors provided additional experiments and clarifications, which addressed many concerns on experimental rigor and robustness. As all the reviewers are positive on this paper after the rebuttal, I would like to recommend acceptance.
Decision: Accept (poster)