In laboratory benchmarks, the best UPGen-generated protocol achieves 252 ms TTFB latency (vs 212 ms Obfs4, 313 ms TLS) and 4.25 Gbit/s throughput per core (vs 4.65 Gbit/s Obfs4, 9.42 Gbit/s TLS). The worst-case UPGen protocol (4.5 RTT handshake) reaches 677 ms TTFB but 3.70 Gbit/s throughput. In large-scale distributed Tor simulations, the choice of UPGen protocol had no statistically significant effect on end-to-end Tor flow performance.

Three open-source DPI tools (Zeek, libprotoident, nDPI) fail to identify 93–100% of UPGen flows across all tools. libprotoident misidentified 7% of UPGen flows as RTMP; nDPI and Zeek produced zero false labels. On a real-world MAWI/WIDE backbone capture, Zeek failed to recognize 90% of flows and nDPI failed on 67%, confirming that unidentified-protocol traffic is common in the wild; allowlisting without significant collateral damage (≥4%) is infeasible.

§4.4, Table 5, Table 6 evaluation

State-of-the-art ML classifiers (Deep Fingerprinting, Decision Tree, Random Forest, nPrintML) trained on known UPGen protocols and benign traffic always incur high out-of-distribution false-positive rates when attempting to block unknown UPGen protocols — in the vast majority of experiments the OOD FPR is 100%. The one exception (SSH OOD, Deep Fingerprinting) achieved a UPGen TPR of only 20%. By contrast, identical classifiers successfully generalize to block unknown Obfs4 flows with near-zero collateral damage in 3 of 4 cases.

§4.3.3, Table 3, Table 4 evaluation

UPGen's generator samples 18 independent parameters to produce 4.2×10^22 distinct structured encrypted protocols (entropy 38.4 bits). Each proxy is assigned a unique generated protocol, so identifying one protocol exposes only a single proxy. The generator was designed by studying 27 real-world encrypted protocols and sampling from observed structural patterns (greeting strings, handshake patterns, field orderings, key encodings).

§2.4, Table 1 defense