Building robust traffic classifier under low quality data: A federated contrastive learning approach

In the big data era, the surge in network traffic volume poses challenges for network management and cybersecurity. Network Traffic Classification (NTC) employs deep learning to categorize traffic data, aiding security and analysis systems as Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). However, current NTC methods, based on isolated network simulations, usually fail to adapt to new protocols and applications and ignore the effects of network conditions and user behavior on traffic patterns. To improve network traffic management insights, federated learning frameworks have been proposed to aggregate diverse traffic data for collaborative model training. This approach faces challenges like data integrity, label noise, packet loss, and skewed data distributions. While label noise can be mitigated through the use of sophisticated traffic labeling tools, other issues such as packet loss and skewed data distributions encountered in Network Packet Brokers (NPB) can severely impede the efficacy of federated learning algorithms. In this paper, we introduced the Robust Traffic Classifier with Federated Contrastive Learning (FC-RTC), combining federated and contrastive learning methods. Using the Supcon-Loss function from contrastive learning, FC-RTC distinguishes between similar and dissimilar samples. Training by sample pairs, FC-RTC effectively updates when receiving corrupted traffic data with packet loss or disorder. In cases of sample imbalance, contrastive loss functions for similar samples reduce model bias towards higher proportion data. By addressing uneven data distribution and packet loss, our system enhances its capability to adapt and perform accurately in real-world network traffic analysis, meeting the specific demands of this complex field.