论文标题
Siamhan：IPv6地址对TLS的相关性攻击通过暹罗异构图注意网络加密流量
SiamHAN: IPv6 Address Correlation Attacks on TLS Encrypted Traffic via Siamese Heterogeneous Graph Attention Network
论文作者
论文摘要
与通常被NAT掩盖的IPv4地址不同，IPv6地址很容易与用户活动相关联，从而危及其隐私。解决了解决此隐私问题的缓解，已经部署了地址与用户相关性的现有方法不可靠。这项工作表明，即使与这些保护机制，对手仍然可以准确地将IPv6地址与用户相关。为此，我们提出了一个IPv6地址相关模型-Siamhan。该模型使用暹罗异质图注意网络，即使用户的流量受TLS加密保护，也可以测量两个IPv6客户端地址是否属于同一用户。使用大型现实世界数据集，我们表明，对于跟踪目标用户并发现唯一用户的任务，最新的技术只能达到85％和60％的精度。但是，Siamhan的精度为99％和88％。
Unlike IPv4 addresses, which are typically masked by a NAT, IPv6 addresses could easily be correlated with user activity, endangering their privacy. Mitigations to address this privacy concern have been deployed, making existing approaches for address-to-user correlation unreliable. This work demonstrates that an adversary could still correlate IPv6 addresses with users accurately, even with these protection mechanisms. To do this, we propose an IPv6 address correlation model - SiamHAN. The model uses a Siamese Heterogeneous Graph Attention Network to measure whether two IPv6 client addresses belong to the same user even if the user's traffic is protected by TLS encryption. Using a large real-world dataset, we show that, for the tasks of tracking target users and discovering unique users, the state-of-the-art techniques could achieve only 85% and 60% accuracy, respectively. However, SiamHAN exhibits 99% and 88% accuracy.
