Abstract:
DNS manipulation is one of the most prevalent and effective techniques for
censoring Internet access and interfering with users’ online activities
worldwide. Reliable detection of DNS tampering is crucial, but challenging
due to evolving censorship tactics and the lack of complete ground truth
data. In this paper, we demonstrate the power of machine learning (ML) in
addressing these challenges by applying supervised and unsupervised models
to recent global DNS measurement data collected by the Open Observatory of
Network Interference (OONI). Our models achieve high accuracy in learning
expert-defined heuristics for DNS tampering and uncovering new manipulation
instances missed by rule-based approaches.
