2015-wang-seeing
findings extracted from this paper
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Format-transforming encryption (FTE) as deployed in the Tor Browser Bundle is detected by combining a URI Shannon-entropy threshold (≥5.5 bits) with an exact URI length check (239 bytes) on the first HTTP GET request. This embellished test produces only 264 false positives across approximately 10 million HTTP URIs in three campus datasets, while a length-only test causes roughly 15% false-positive rate over the same flows.
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CART decision-tree classifiers trained on entropy-based and packet-header features detect all five Tor pluggable transports (obfsproxy3/4, FTE, meek-amazon, meek-google) with average PR-AUC=0.987, TPR=0.986, and FPR=0.003 on synthetic traces. On 14 million real campus flows the highest per-obfuscator FPR is 0.65%, and meek-google yields only 842 false positives across all three datasets. However, cross-environment portability is poor: classifiers trained on an Ubuntu/campus setup and tested on a Windows/home network achieve true-positive rates as low as 52% with false-positive rates reaching 12%.
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The paper demonstrates that 'having no fingerprint is itself a fingerprint': randomizing obfuscators that emit uniformly random bytes from the first packet are detectable precisely because conventional protocols (TLS, SSH, HTTP) always begin with fixed plaintext headers. This structural distinction requires no deep payload parsing — the attack operates on only the first TCP packet — and achieves TPR=1.0 / FPR=0.002 against obfsproxy3/4 using commodity-implementable statistics.
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Obfsproxy3 and obfsproxy4 are reliably detected by an entropy-distribution test (KS test, block size k=8) applied to the first 2,048 bytes of the first client-to-server packet, combined with a minimum payload-length check of 149 bytes. On three university campus datasets totaling over 14 million TCP flows, the test achieves TPR=1.0 with FPR ranging from 0.24% to 0.33%. Omitting the length check raises the SSL/TLS false-positive rate to approximately 23%.
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A semantics-based attack that flags HTTP flows carrying structurally invalid PDF documents as Stegotorus produces false-positive rates as high as 43% across three campus datasets (10,847 PDF flows examined), because malformed, partial, and non-standard PDFs are common in real network traffic. By contrast, active HTTP-response fingerprinting of a suspected Stegotorus server yields only 0.03% false positives (3 matching servers out of 9,320 Alexa-top-10K servers), but requires active probing and is detectable by the proxy operator.