Accurate TLS Fingerprinting using Destination Context and Knowledge Bases

2020/09/03 by Blake Anderson, Anderson, Blake, David McGrew +1 · 2 citations
Computer Science · #Internet Traffic Analysis and Secure E-voting #Network Security and Intrusion Detection #Spam and Phishing Detection

paper · pdf · doi:10.48550/arxiv.2009.01939

Abstract

Network fingerprinting is used to identify applications, provide insight into network traffic, and detect malicious activity. With the broad adoption of TLS, traditional fingerprinting techniques that rely on clear-text data are no longer viable. TLS-specific techniques have been introduced that create a fingerprint string from carefully selected data features in the client_hello to facilitate process identification before data is exchanged. Unfortunately, this approach fails in practice because hundreds of processes can map to the same fingerprint string. We solve this problem by presenting a TLS fingerprinting system that makes use of the destination address, port, and server name in addition to a carefully constructed fingerprint string. The destination context is used to disambiguate the set of processes that match a fingerprint string by applying a weighted naive Bayes classifier, resulting in far greater performance.

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