RT Journal Article
T1 Encrypted Traffic Classification Based on Unsupervised Learning in Cellular Radio Access Networks
A1 Gijón, Carolina
A1 Toril-Genovés, Matías
A1 Solera-Delgado, Marta
A1 Luna-Ramírez, Salvador
A1 Jiménez Pérez, Luis Roberto
K1 Sistemas de comunicaciones inalámbricos
AB Traffic classification will be a key aspect in the operation of future 5G cellular networks, where services of very different nature will coexist. Unfortunately, data encryption makes this task very difficult. To overcome this issue, flow-based schemes have been proposed based on payload-independent features extracted from the Internet Protocol (IP) traffic flow. However, such an approach relies on the use of expensive traffic probes in the core network. Alternatively, in this paper, an offline method for encrypted traffic classification in the radio interface is presented. The method divides connections per service class by analyzing only features in radio connection traces collected by base stations. For this purpose, it relies on unsupervised learning, namely agglomerative hierarchical clustering. Thus, it can be applied in the absence of labeled data (seldom available in operational cellular networks). Likewise, it can also identify new services launched in the network. Method assessment is performed over a real trace dataset taken from a live Long Term Evolution (LTE) network. Results show that traffic shares per application class estimated by the proposed method are similar to those provided by a vendor report
PB IEEE
YR 2020
FD 2020-09-09
LK https://hdl.handle.net/10630/37544
UL https://hdl.handle.net/10630/37544
LA eng
NO C. Gijón, M. Toril, M. Solera, S. Luna-Ramírez and L. R. Jiménez, "Encrypted Traffic Classification Based on Unsupervised Learning in Cellular Radio Access Networks," in IEEE Access, vol. 8, pp. 167252-167263, 2020, doi: 10.1109/ACCESS.2020.3022980
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026