FLAGB: Focal Loss based Adaptive Gradient Boosting for Imbalanced Traffic Classification
Yu Guo, Zhenzhen Li, Zhen Li, Gang Xiong, Minghao Jiang, Gaopeng Gou
Abstract
Machine learning (ML) is widely applied to network traffic classification (NTC), which is an essential component for network management and security. While the imbalance distribution exhibiting in real-world network traffic degrades the classifier's performance and leads to prediction bias towards majority classes, which is always ignored by exiting ML-based NTC studies. Some researches have proposed solutions such as resampling for imbalanced traffic classification. However, most methods don't take traffic characteristics into account and consume much time, resulting in unsatisfactory results. In this paper, we analyze the imbalanced traffic data and propose the focal loss based adaptive gradient boosting framework (FLAGB) for imbalanced traffic classification. FLAGB can automatically adapt to NTC tasks with different imbalance levels and overcome imbalance without the prior knowledge of data distribution. Our comprehensive experiments on two network traffic datasets covering binary and multiple classes prove that FLAGB outperforms the state-of-the-art methods. Its low time consumption during training also makes it an excellent choice for highly imbalanced traffic classification.