TY - ADVS
T1 - Datasets for Cache-based and Network-based Traffic and Application Characterization.
AU - Shusterman, Anatoly
AU - Finkelstein, Chen
AU - Gruner, Ofir
AU - Shani, Yarin
AU - Oren, Yossi
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2021
Y1 - 2021
N2 - It is important for network operators to carry out traffic and application characterization to gain insights into the activity of their networks. Several studies proposed methods that extract features from network traffic to characterize it, or to classify the application that produced it, based on a ?man in the middle? network interception point that can analyze the entire network traffic of an organization. This network topology, however, is increasingly becoming irrelevant, due to mobile and remote traffic joining the corporate network by passing through VPN channels or relay networks. In this work we propose an edge-oriented lightweight traffic characterization method, based on measuring contention on the last-level CPU cache. In contrast to previous traffic characterization methods, which track network traffic from a central location, our method performs measurements directly on user machines, using an unprivileged JavaScript-based webpage. Our evaluation shows that the accuracy of our cache-based method is equivalent to that of network-based methods, both over VPN and over non-VPN networks.
AB - It is important for network operators to carry out traffic and application characterization to gain insights into the activity of their networks. Several studies proposed methods that extract features from network traffic to characterize it, or to classify the application that produced it, based on a ?man in the middle? network interception point that can analyze the entire network traffic of an organization. This network topology, however, is increasingly becoming irrelevant, due to mobile and remote traffic joining the corporate network by passing through VPN channels or relay networks. In this work we propose an edge-oriented lightweight traffic characterization method, based on measuring contention on the last-level CPU cache. In contrast to previous traffic characterization methods, which track network traffic from a central location, our method performs measurements directly on user machines, using an unprivileged JavaScript-based webpage. Our evaluation shows that the accuracy of our cache-based method is equivalent to that of network-based methods, both over VPN and over non-VPN networks.
U2 - 10.21227/bq8s-ps56
DO - 10.21227/bq8s-ps56
M3 - Software
ER -