KiHoLee
138897aa8d
The package was missing every script behind the KM (lrn.) curves and both learned table rows: exp_learned's driver, the merge that folds the learned rows into sec_compare.csv and refresh_summary.csv, and the report that reads the learned numbers back. It was also missing sec_keylen_perm.csv, so Fig. 3 could not be regenerated at all, and the two diagnostics that answer why a fixed key beats a learned one here and where a learned mask would win instead. The learned artifacts themselves are regenerated. They were trained on the cross-entropy alone, which drifts to disjoint sparse supports: 99 percent of each key's energy on about six of the 64 entries, so a digit is decided over a sixth of its period and the key set is a choice of support rather than a dense direction in R^L. They are now the regularized keys of Section V-C, and check_consistency asserts which of the two families the figures draw. Verified from inside this repository: replot_security.py rebuilds all seven result figures, make_tables.py reproduces both result tables, and check_consistency.py passes every check that does not need the manuscript. The README now lists what ships. Its run list, layout and figure map had none of the learned pipeline, named two tables the manuscript renders as prose, and gave Fig. 3 no data file for its permutation curve.
107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
# -*- coding: utf-8 -*-
|
|
"""Every learned-family number the manuscript quotes, read from data/.
|
|
|
|
Switching the learned family from the unpenalized keys to the
|
|
regularized ones of Section V-C moves every learned value in the paper.
|
|
This prints them next to their structured counterparts so the sentences
|
|
that carry them can be updated from one place, and so a later rerun can
|
|
be checked against what is printed.
|
|
|
|
Run: python code/report_learned.py
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
import csv
|
|
import json
|
|
from pathlib import Path
|
|
|
|
DATA = Path(__file__).resolve().parents[1] / "data"
|
|
|
|
|
|
def rows(name):
|
|
with open(DATA / name) as f:
|
|
return list(csv.DictReader(f))
|
|
|
|
|
|
def at(rs, key, val, col):
|
|
for r in rs:
|
|
if abs(float(r[key]) - val) < 1e-9:
|
|
return float(r[col])
|
|
raise KeyError("%s=%s not in the sweep" % (key, val))
|
|
|
|
|
|
def main():
|
|
print("== Fig. 2, SER against SNR ==")
|
|
s = rows("sec_snr.csv")
|
|
l = rows("sec_snr_learned.csv")
|
|
ratios = []
|
|
for a, b in zip(s, l):
|
|
r = float(b["legit"]) / float(a["legit"])
|
|
ratios.append(r)
|
|
print(" %5s dB str %.5f lrn %.5f ratio %.3f"
|
|
% (a["snr_db"], float(a["legit"]), float(b["legit"]), r))
|
|
print(" ratio range %.3f to %.3f" % (min(ratios), max(ratios)))
|
|
|
|
print("\n== Fig. 3, SER against key length ==")
|
|
s = rows("sec_keylen.csv")
|
|
l = rows("sec_keylen_learned.csv")
|
|
for a, b in zip(s, l):
|
|
print(" L=%-4s str %.5f lrn %.5f ratio %.3f kappa %.5f"
|
|
% (a["L"], float(a["legit_ser"]), float(b["legit_ser"]),
|
|
float(b["legit_ser"]) / float(a["legit_ser"]),
|
|
float(b["mask_xcorr"])))
|
|
print(" learned outsider floor %.5f"
|
|
% min(float(r["eve_ser"]) for r in l))
|
|
|
|
print("\n== Fig. 4, jamming at JSR 0 dB ==")
|
|
print(" str blind %.4f" % at(rows("sec_jam.csv"), "jsr_db", 0.0,
|
|
"blind"))
|
|
print(" lrn blind %.4f" % at(rows("sec_jam_learned.csv"), "jsr_db",
|
|
0.0, "blind"))
|
|
|
|
print("\n== Fig. 6, best of K=1e6 guesses ==")
|
|
print(" str %.4f" % at(rows("sec_brute_cmp.csv"), "K", 1e6, "ser_mask"))
|
|
print(" lrn %.4f" % at(rows("sec_brute_learned.csv"), "K", 1e6,
|
|
"ser_mask"))
|
|
|
|
print("\n== Fig. 7, known plaintext at 10 dB ==")
|
|
for name in ("kpa.csv", "kpa_learned.csv"):
|
|
r = [x for x in rows(name) if float(x["snr_db"]) == 10.0]
|
|
print(" %-16s N=2 %.4f N=8 %.4f N=64 %.4f"
|
|
% (name, at(r, "n_frames", 2, "eve_ser"),
|
|
at(r, "n_frames", 8, "eve_ser"),
|
|
at(r, "n_frames", 64, "eve_ser")))
|
|
|
|
print("\n== Fig. 8, real token streams ==")
|
|
s = rows("real_sec_ter.csv")
|
|
l = rows("real_sec_ter_learned.csv")
|
|
gaps = []
|
|
for a, b in zip(s, l):
|
|
for col in ("ter_eve", "ter_insider"):
|
|
gaps.append(abs(float(a[col]) - float(b[col])))
|
|
print(" %4s dB legit str %.5f lrn %.5f ratio %.3f"
|
|
% (a["snr_db"], float(a["ter_legit"]), float(b["ter_legit"]),
|
|
float(b["ter_legit"]) / float(a["ter_legit"])))
|
|
print(" largest adversary gap between families %.2e" % max(gaps))
|
|
for name in ("real_sec_stats.json", "real_sec_stats_learned.json"):
|
|
if (DATA / name).exists():
|
|
d = json.loads((DATA / name).read_text())
|
|
print(" %-28s %s" % (name, {k: d[k] for k in list(d)[:6]}))
|
|
|
|
print("\n== Table IV, scheme comparison ==")
|
|
for name in ("sec_compare.csv", "compare_learned.csv"):
|
|
for r in rows(name):
|
|
print(" %-20s %s" % (name, dict(r)))
|
|
|
|
print("\n== Table V, refresh ==")
|
|
l = rows("refresh_learned.csv")
|
|
print(" lrn legit %.5f to %.5f"
|
|
% (min(float(r["legit_ser"]) for r in l),
|
|
max(float(r["legit_ser"]) for r in l)))
|
|
print(" lrn eve %.5f" % (sum(float(r["eve_ser"]) for r in l)
|
|
/ len(l)))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|