A New Transfer Learning-Based Traffic Classification Algorithm for a Multi-Domain SDN Network
Nam-Thang Hoang, Cong-Son Duong, Minh-Ngoc Vu, Huy-Hieu Nguyen, Truong X. Tran, Van Tong, Hai Anh Tran · 2023
To enhance the efficiency and resource utilization of a computer network, it is imperative to classify network traffic and implement distinct priority policies. Network traffic classification plays a pivotal role across various domains, including network administration, cybersecurity, and network resource optimization. As encrypted network data undergoes diverse evolution, as evident in datasets from tech giants like Google, Facebook, and YouTube, traditional traffic classification methods have given way to machine learning-based approaches. Given that computer networks are primarily deployed as distributed multi-domain systems, employing machine learning for traffic classification becomes challenging when a new network domain appears with a limited dataset. One potential remedy is to employ transfer learning, allowing knowledge transfer from a pre-trained model in an established domain to a new one. In this paper, we present two contributions. First, a novel algorithm called Multi-class TrAdaBoost-CNN is introduced to tackle the challenge of cross-domain classification in encrypted network services. This algorithm extends the Multi-class TrAdaBoost approach by incorporating a Convolutional Neural Network (CNN) as a weak learner. Secondly, extensive experiments are conducted on two distinct domains characterized by imbalanced data distributions to assess the efficacy of our proposed method. The experimental results clearly demonstrate that our algorithm outperforms the traditional CNN model, achieving remarkable accuracy improvements of up to 16%, even when dealing with extremely limited data.