FairNet: A Measurement Framework for Traffic Discrimination Detection on the Internet

• Published in: IEEE eTransactions on network and service management 2023-12, Vol.20 (4), p.1-1
• Main Authors: Khandkar, Vinod, Hanawal, Manjesh
• Format: Article
• Language: English
• Subjects: Algorithms; Applications programs; Degradation; HTTPS traffic; Internet; Internet access; Mobile computing; Net neutrality; Payloads; Probes; Quality of service; Servers; Service Name Indication (SNI); Throughput; Traffic congestion; Traffic Discrimination; Traffic shapers; Video on demand
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2023.3263373

Network neutrality is related to the nondiscriminatory treatment of packets on the Internet. Any deliberate discrimination of traffic of one application while favoring others violates the principle of neutrality. Many countries have enforced laws against such discrimination. One requires tools to detect any net neutrality violations to enforce such laws. However, detecting such violations is challenging as it is hard to separate any degradation in quality due to natural network effects and selective degradation. Also, legitimate traffic management and deliberate discrimination methods can be technically the same, making it challenging to distinguish them further. We developed an end-to-end measurement framework named FairNet to detect discrimination of traffic. It compares the performance of similar services. Our focus is on HTTPS streaming services which constitute a predominant portion of the Internet traffic. The effect of confounding factors (congestion, traffic management policy, dynamic rate adaptation) is made similar on the test services to ensure a fair comparison. FairNet framework uses a replay server and user-client that exchanges correctly identifiable traffic streams over the Internet. The Server Name Indication (SNI) field in the TLS handshake, which goes in plain text, ensures that the traffic from the replay server appears to network middle-boxes as that coming from its actual server. We validated that appropriate SNIs result in the correct classification of services using a commercial traffic shaper. FairNet uses two novel algorithms based on application-level throughput and connection status to detect traffic discrimination. We also validated the methodologys effectiveness by collecting network logs through mobile apps over the live Internet and analyzing them.