Quality of Service (QoS) management in IP networks today relies on static configuration of classes of service definitions and related forwarding priorities. Packets are actually classified according to the DiffServ architecture based on the RFC 4594, typically thanks to static configuration or filters matching packet features, at network access equipment. In this paper, we propose a dynamic classification procedure, referred to as Learning-powered DiffServ (L-DiffServ), able to detect the distinctive characteristics of traffic and to dynamically assign service classes to IP packets. The idea is to apply semi-unsupervised Machine Learning techniques, such as Linear Discriminant Analysis (LDA) and K-Means, with a proper customization to take into account the issues related to packet-level analysis, i.e. unbalanced distribution of traffic among classes and selection of proper IP header related features. The performance evaluation highlights that L-DiffServ is able to change dynamically the classification outcome, providing an higher number of classes than DiffServ. This last result represents the first step toward a more granular differentiation of IP traffic.