# Autonomous Unknown-Application Filtering and Labeling for DL-based Traffic Classifier Update

arXiv: https://arxiv.org/abs/2002.06359 · HTML: https://mlsanity.com/paper/2002.06359/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2020.

## Built on by (most cited first, 16 of 16)

- [NetGPT: Generative Pretrained Transformer for Network Traffic](https://mlsanity.com/paper/2304.09513/) (2023)
- [Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications](https://mlsanity.com/paper/2104.03182/) (2021)
- [On the Local Cache Update Rules in Streaming Federated Learning](https://mlsanity.com/paper/2303.16340/) (2023)
- [A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classification](https://mlsanity.com/paper/2107.04464/) (2021)
- [Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach](https://mlsanity.com/paper/2302.00207/) (2023)
- [FlowletFormer: Network Behavioral Semantic Aware Pre-training Model for Traffic Classification](https://mlsanity.com/paper/2508.19924/) (2025)
- [Quality Monitoring and Assessment of Deployed Deep Learning Models for Network AIOps](https://mlsanity.com/paper/2202.13642/) (2022)
- [Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks](https://mlsanity.com/paper/2205.08347/) (2022)
- [NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics](https://mlsanity.com/paper/2412.20635/) (2024)
- [Revolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph Model](https://mlsanity.com/paper/2501.03279/) (2025)
- [Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks](https://mlsanity.com/paper/2104.10400/) (2021)
- [Thinkback: Task-SpecificOut-of-Distribution Detection](https://mlsanity.com/paper/2107.06668/) (2021)
- [One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning](https://mlsanity.com/paper/2402.07501/) (2024)
- [Efficiently and Effectively: A Two-stage Approach to Balance Plaintext and Encrypted Text for Traffic Classification](https://mlsanity.com/paper/2407.19687/) (2024)
- [Mean Masked Autoencoder with Flow-Mixing for Encrypted Traffic Classification](https://mlsanity.com/paper/2603.29537/) (2026)
- [Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling](https://mlsanity.com/paper/2608.15465/) (2026)

## Abstract (arXiv)

Network traffic classification has been widely studied to fundamentally advance network measurement and management. Machine Learning is one of the effective approaches for network traffic classification. Specifically, Deep Learning (DL) has attracted much attention from the researchers due to its effectiveness even in encrypted network traffic without compromising neither user privacy nor network security. However, most of the existing models are created from closed-world datasets, thus they can only classify those existing classes previously sampled and labeled. In this case, unknown classes cannot be correctly classified. To tackle this issue, an autonomous learning framework is proposed to effectively update DL-based traffic classification models during active operations. The core of the proposed framework consists of a DL-based classifier, a self-learned discriminator, and an autonomous self-labeling model. The discriminator and self-labeling process can generate new dataset during active operations to support classifier update. Evaluation of the proposed framework is performed on an open dataset, i.e., ISCX VPN-nonVPN, and independently collected data packets. The results demonstrate that the proposed autonomous learning framework can filter packets from unknown classes and provide accurate labels. Thus, corresponding DL-based classification models can be updated successfully with the autonomously generated dataset.