Really interesting work. After going through the experiment and the analysis pipeline, one direction that seems like a natural extension would be to replace or complement the manually defined heuristics with a supervised machine learning model.
Most of the signals you are already extracting, such as initial flight sizes, entropy, directionality, packet sizes, inter-packet gaps, burst statistics, retransmissions, and multi-scale temporal features, could be used directly as model features.
Warnet also seems particularly well suited for generating a much larger labeled dataset. In principle, it should be possible to generate a large number of BIP324 connections while varying topology, latency, jitter, packet loss, transaction activity, garbage length, node behavior, and other network conditions, and combine them with large amounts of non-BIP324 traffic.
I think the negative examples would be particularly important. Rather than relying mainly on easy negatives, I would include many hard negatives such as obfs4, Tor, TLS/HTTPS, QUIC, and other encrypted or obfuscated protocols. The fact that obfs4 already produced a false positive in the current heuristic makes this especially interesting.
A first experiment could be relatively simple:
PCAP → existing pipeline features → classifier → P(BIP324)
Then, models such as logistic regression, Random Forest, XGBoost, and an MLP could be compared against the current heuristic baseline.