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Machine Learning Interpretability Meets TLS Fingerprinting
[article]
2021
arXiv
pre-print
Protecting users' privacy over the Internet is of great importance; however, it becomes harder and harder to maintain due to the increasing complexity of network protocols and components. Therefore, investigating and understanding how data is leaked from the information transmission platforms and protocols can lead us to a more secure environment. In this paper, we propose a framework to systematically find the most vulnerable information fields in a network protocol. To this end, focusing on
arXiv:2011.06304v2
fatcat:gb3memyoivhtbnkx5fxrwu4ibu