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Unlike IPv4 addresses, which are typically masked by a NAT, IPv6 addressescould easily be correlated with user activity, endangering their privacy.Mitigations to address this privacy concern have been deployed, making existingapproaches for address-to-user correlation unreliable. This work demonstratesthat an adversary could still correlate IPv6 addresses with users accurately,even with these protection mechanisms. To do this, we propose an IPv6 addresscorrelation model - SiamHAN. The model uses a Siamese Heterogeneous GraphAttention Network to measure whether two IPv6 client addresses belong to thesame user even if the user's traffic is protected by TLS encryption. Using alarge real-world dataset, we show that, for the tasks of tracking target usersand discovering unique users, the state-of-the-art techniques could achieveonly 85% and 60% accuracy, respectively. However, SiamHAN exhibits 99% and 88%accuracy.

SiamHAN: IPv6 Address Correlation Attacks on TLS Encrypted Traffic via Siamese Heterogeneous Graph Attention Network