Network traffic classification via non-convex multi-task feature learning

Machine learning has been used in network traffic classification and statistical features are used to represent flows. However, conventional feature selection may work out in face of dynamic and complex traffic data. Multi-Task Learning has obtained quite wide attention nowadays, and one important form of multi-task learning is to exploit the features shared by tasks by sparse models. We propose a fast multi-task sparse feature learning method, using a non-convex Capped-ℓ1,ℓ1ℓ1,ℓ1 as the regularizer to learn a set of shared features in traffic data. Specifically, the non-convex multi-task feature learning model can learn features belonging to each task as well as the common features shared among tasks. We use the iterative shrinkage and thresholding (IST) algorithm to solve the problem, which has a closed-form solution for one of the crucial steps in the whole iteration. Experiment on real traffic data captured from backbone network as well as synthetic data and other popular real-world data show the effectiveness the method, compared with state-of-the-art methods.