Audit round: fair OMA reference, dense grids, covariance-attack checks
Resource-match the OMA reference in the key-length sweep (oma_ser_keylen), which gives it the L/16 combining gain the longer frame allows. The proposal now passes a resource-matched OMA by 1.27x at L=64 rather than the 4.3x reported against a fixed-d reference. Densify the JSR, sensitivity, and brute-force grids so the curves are smooth, give the index cipher its channel floor instead of error-free reception, and add the permutation-key known-plaintext attack (exp_permkpa) so Fig. 7 carries a conventional linear scheme. Add check_cov_attack.py and check_cov_ceiling.py: a referee raised a ciphertext-only second-order attack; the exact-population test shows the received covariance leaks only a sparse rank-deficient subset of the key Gram and leaves the eavesdropper at the random-guess level. Dump verify_math.csv, move the superseded V=256 pilot CSVs to data/pilot.
This commit is contained in:
KiHoLee
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@@ -3,20 +3,22 @@ from ../data/*.csv and writes paper-ready PDFs to ../fig/. No experiment
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is rerun. All result plots share one canvas and axes rectangle (8:6 box).
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Label dictionary is fixed here and copied verbatim into tables and prose.
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fig_sec_snr.pdf : legitimate and outsider SER vs SNR (Fig. 2)
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fig_sec_snr.pdf : legitimate and eavesdropper SER vs SNR (Fig. 2)
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fig_sec_keylen.pdf : SER vs key length L (Fig. 3)
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fig_sec_jam.pdf : target-user SER vs JSR, four schemes (Fig. 4)
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fig_sec_sens.pdf : outsider SER vs fraction of key held (Fig. 5)
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fig_sec_brute.pdf : outsider SER vs number of key guesses (Fig. 6)
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fig_sec_kpa.pdf : outsider SER vs known-plaintext frames (Fig. 7)
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fig_sec_sens.pdf : eavesdropper SER vs fraction of key held (Fig. 5)
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fig_sec_brute.pdf : eavesdropper SER vs number of key guesses (Fig. 6)
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fig_sec_kpa.pdf : eavesdropper SER vs known-plaintext frames (Fig. 7)
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fig_sec_real.pdf : token error rate on real streams (Fig. 8)
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fig_sec_brute_rho.pdf is also emitted as a diagnostic and is not used in
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the paper.
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Curves that coincide by construction are drawn deliberately layered, the
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lower one wide and semi-transparent and the upper one narrow with open
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markers, so every legend entry has a visible curve.
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"""
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from __future__ import annotations
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from pathlib import Path
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import csv
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import math
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import matplotlib
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matplotlib.use("Agg")
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@@ -32,7 +34,7 @@ plt.rcParams.update({
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"font.serif": ["DejaVu Serif", "Times New Roman"],
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"font.size": 9,
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"axes.labelsize": 9,
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"legend.fontsize": 6.6,
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"legend.fontsize": 7.4,
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"xtick.labelsize": 8,
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"ytick.labelsize": 8,
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"axes.grid": True,
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@@ -57,13 +59,20 @@ C_PUB = "#16a085"
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LBL = {
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"legit": "Legitimate",
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"oma": "OMA",
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"eve_pub": "Eve, public masks",
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"eve_key": "Eve, wrong key",
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"eve_pub": "Eavesdropper, public masks",
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"eve_key": "Eavesdropper, wrong key",
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"chance": "Random guess",
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"jam_m": "Matched jammer (public masks)",
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"jam_b": "Blind jammer (proposed)",
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"nojam": "No jammer",
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"mask": "Keyed masking",
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"perm": "Permutation key",
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"pad": "Index cipher",
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"insider": "Insider",
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"outsider": "Outsider",
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}
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# deliberate-layering style for the LOWER of two coinciding curves
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UNDER = dict(lw=2.6, ms=7, alpha=0.85)
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# and for the curve riding on top of it
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OVER = dict(lw=1.2, ms=4.5, mfc="none")
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def load(name):
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@@ -106,10 +115,11 @@ def fig_snr():
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r = load("sec_snr.csv")
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x = col(r, "snr_db")
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fig, ax = plt.subplots()
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# legitimate and OMA coincide by construction; layered deliberately
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ax.semilogy(x, col(r, "legit"), color=C_LEGIT, marker="o", ls="-",
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label=LBL["legit"])
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label=LBL["legit"], **UNDER)
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ax.semilogy(x, col(r, "oma"), color=C_OMA, marker="^", ls=":",
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label=LBL["oma"])
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label=LBL["oma"], **OVER)
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ax.semilogy(x, col(r, "eve_public"), color=C_PUB, marker="v",
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ls="none", markersize=5.2, markerfacecolor="none",
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label=LBL["eve_pub"])
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@@ -125,13 +135,17 @@ def fig_snr():
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def fig_keylen():
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"""The OMA reference is the resource-matched one of oma_ser_keylen,
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which is undefined at key lengths where 16/L is not an integer; those
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rows carry nan and are skipped."""
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r = load("sec_keylen.csv")
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x = col(r, "L", int)
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fig, ax = plt.subplots()
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ax.semilogy(x, col(r, "legit_ser"), color=C_LEGIT, marker="o", ls="-",
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label=LBL["legit"])
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ax.semilogy(x, col(r, "oma"), color=C_OMA, marker="^", ls=":",
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label=LBL["oma"])
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op = [(l, v) for l, v in zip(x, col(r, "oma")) if not math.isnan(v)]
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ax.semilogy([p[0] for p in op], [p[1] for p in op], color=C_OMA,
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marker="^", ls=":", label=LBL["oma"])
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ax.semilogy(x, col(r, "eve_ser"), color=C_EVE, marker="s", ls="--",
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label=LBL["eve_key"])
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ax.set_xlabel("Key length $L$")
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@@ -144,47 +158,47 @@ def fig_keylen():
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def fig_jam():
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"""Target-user SER against JSR for four schemes. A linear axis is
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used because the range spans less than one decade, where a log axis
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would print wide minor tick labels that crowd out the y label."""
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would print wide minor tick labels that crowd out the y label. The
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no-jammer reference is annotated on the line rather than listed in
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the legend, so the legend never covers it."""
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r = load("sec_jam_cmp.csv")
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x = col(r, "jsr_db")
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me = max(1, len(x) // 8)
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fig, ax = plt.subplots()
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ax.plot(x, col(r, "oma_targeted"), color=C_PUB, marker="^", ls=":",
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label="OMA, targeted")
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ax.plot(x, col(r, "matched"), color=C_MATCH, marker="P", ls="--",
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label="Public masks, matched")
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# the two blind curves agree to 0.0015, so the proposed one is drawn
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# first and wide and the permutation key rides on top with open
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# markers, otherwise one legend entry would have no visible curve
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ax.plot(x, col(r, "blind"), color=C_LEGIT, marker="o", ls="-", lw=2.6,
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ms=7, alpha=0.85, label="Proposed, blind")
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markevery=me, label=LBL["mask"] + ", matched")
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ax.plot(x, col(r, "oma_targeted"), color=C_PUB, marker="^", ls=":",
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markevery=me, label=LBL["oma"] + ", targeted")
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# the two blind curves agree to 0.0015; deliberate layering
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ax.plot(x, col(r, "blind"), color=C_LEGIT, marker="o", ls="-",
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markevery=me, label=LBL["mask"] + ", blind", **UNDER)
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ax.plot(x, col(r, "perm_blind"), color=C_EVE, marker="s", ls="-.",
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lw=1.2, ms=4.5, mfc="none", label="Permutation key, blind")
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markevery=me, label=LBL["perm"] + ", blind", **OVER)
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nojam = float(load("sec_jam.csv")[0]["nojam"])
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ax.axhline(nojam, color=C_OMA, ls=(0, (1, 3)), lw=0.9,
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label=LBL["nojam"])
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ax.axhline(nojam, color=C_OMA, ls=(0, (1, 3)), lw=0.9)
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ax.text(max(x) - 0.6, nojam + 0.02, LBL["nojam"], ha="right",
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va="bottom", fontsize=7.4, color="#555555")
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ax.set_xlabel("JSR (dB)")
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ax.set_ylabel("SER")
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ax.set_xlim(min(x), max(x))
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ax.set_ylim(0.2, 1.02)
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ax.legend(loc="lower right")
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ax.legend(loc="center right", bbox_to_anchor=(0.985, 0.47))
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save(fig, "fig_sec_jam")
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def fig_sens():
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"""Key sensitivity of three schemes on one axis, the fraction of the
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key the attacker holds. For keyed masking that fraction is the mask
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correlation, for the permutation scheme the fraction of positions
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placed correctly, for the index cipher the fraction of pad bits
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known."""
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key the attacker holds. All three ride the random-guess level over
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most of the range, so the flat region is deliberately layered."""
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r = load("sec_sens_cmp.csv")
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x = col(r, "frac")
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fig, ax = plt.subplots()
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ax.plot(x, col(r, "ser_mask"), color=C_LEGIT, marker="o", ls="-",
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label="Keyed masking")
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label=LBL["mask"], **UNDER)
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ax.plot(x, col(r, "ser_perm"), color=C_EVE, marker="s", ls="--",
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label="Permutation key")
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label=LBL["perm"], **OVER)
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ax.plot(x, col(r, "ser_pad"), color=C_PUB, marker="v", ls="-.",
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label="Index cipher")
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lw=1.2, ms=4.5, mfc="none", label=LBL["pad"])
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chance = 1.0 - (1.0 / 16.0) ** 4
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ax.axhline(chance, color=C_CH, ls=":", lw=0.9, label=LBL["chance"])
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ax.set_xlabel("Fraction of the key recovered")
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@@ -200,54 +214,35 @@ def fig_brute():
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r = load("sec_brute_cmp.csv")
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x = col(r, "K")
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fig, ax = plt.subplots()
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ax.semilogx(x, col(r, "ser_mask"), color=C_LEGIT, marker="o", ls="-",
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label="Keyed masking")
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ax.semilogx(x, col(r, "ser_perm"), color=C_EVE, marker="s", ls="--",
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label="Permutation key")
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label=LBL["perm"], **UNDER)
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ax.semilogx(x, col(r, "ser_pad"), color=C_PUB, marker="v", ls="-.",
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label="Index cipher")
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label=LBL["pad"], **OVER)
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ax.semilogx(x, col(r, "ser_mask"), color=C_LEGIT, marker="o", ls="-",
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label=LBL["mask"])
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kl = load("sec_keylen.csv")
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legit = float([q for q in kl if int(q["L"]) == 16][0]["legit_ser"])
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ax.axhline(legit, color=C_OMA, ls=":", lw=0.9, label=LBL["legit"])
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ax.set_xlabel("Number of key guesses $K$")
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ax.set_ylabel("Eavesdropper SER")
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ax.set_ylim(-0.03, 1.05)
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ax.legend(loc="center left")
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ax.set_ylim(0.2, 1.05)
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ax.legend(loc="lower left")
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save(fig, "fig_sec_brute")
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def fig_brute_rho():
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"""Best key correlation a search of size K reaches, per key length.
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This is a property of the key space alone."""
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r = load("sec_brute.csv")
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fig, ax = plt.subplots()
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sty = {8: ("#c0392b", "o"), 16: ("#2c5fa8", "s"),
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32: ("#16a085", "v"), 64: ("#8e44ad", "P")}
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for Lp, (c, mk) in sty.items():
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rows = [row for row in r if int(row["L"]) == Lp]
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ax.semilogx([float(x["K"]) for x in rows],
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[float(x["best_rho"]) for x in rows],
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color=c, marker=mk, ls="-", label=f"$L={Lp}$")
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ax.axhline(0.96, color=C_CH, ls="-.", lw=0.9, label="Break threshold")
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ax.set_xlabel("Number of key guesses $K$")
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ax.set_ylabel(r"Best key correlation $\kappa$")
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ax.set_ylim(0, 1.05)
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ax.legend(loc="upper left")
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save(fig, "fig_sec_brute_rho")
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def fig_real():
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r = load("real_sec_ter.csv")
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x = col(r, "snr_db")
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fig, ax = plt.subplots()
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# legitimate/OMA and insider/outsider coincide pairwise; layered
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ax.semilogy(x, col(r, "ter_legit"), color=C_LEGIT, marker="o", ls="-",
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label=LBL["legit"])
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label=LBL["legit"], **UNDER)
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ax.semilogy(x, col(r, "ter_oma"), color=C_OMA, marker="^", ls=":",
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label=LBL["oma"])
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label=LBL["oma"], **OVER)
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ax.semilogy(x, col(r, "ter_insider"), color=C_PUB, marker="v", ls="-.",
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label="Insider")
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lw=2.6, alpha=0.85, ms=7, label=LBL["insider"])
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ax.semilogy(x, col(r, "ter_eve"), color=C_EVE, marker="s", ls="--",
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label=LBL["eve_key"])
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label=LBL["outsider"], **OVER)
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ax.set_xlabel("SNR (dB)")
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ax.set_ylabel("TER")
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ax.set_xlim(min(x), max(x))
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@@ -256,6 +251,9 @@ def fig_real():
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def fig_kpa():
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"""Known-plaintext recovery of the keyed masks at three collection
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SNRs, with the permutation key under the same attack as the linear
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comparison scheme."""
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r = load("kpa.csv")
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fig, ax = plt.subplots()
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sty = {0.0: ("#c0392b", "o"), 10.0: ("#2c5fa8", "s"),
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@@ -265,7 +263,13 @@ def fig_kpa():
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n = [float(row["n_frames"]) for row in rows]
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ser = [float(row["eve_ser"]) for row in rows]
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ax.semilogx(n, ser, color=c, marker=mk, ls="-",
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label=f"{int(snr)} dB")
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label=LBL["mask"] + f", {int(snr)} dB")
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try:
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p = load("pkpa.csv")
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ax.semilogx(col(p, "n_frames"), col(p, "eve_ser"), color=C_MATCH,
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marker="P", ls="--", label=LBL["perm"] + ", 20 dB")
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except FileNotFoundError:
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print("[skip] pkpa.csv not present yet")
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# legitimate reference measured with the SAME estimator as the
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# eavesdropper curves, namely the four-user average of eval_ser_sse
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# at L=16, taken from sec_keylen.csv rather than from the user-1
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@@ -287,7 +291,6 @@ def main():
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try:
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fig_sens()
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fig_brute()
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fig_brute_rho()
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except FileNotFoundError:
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print("[skip] attack-difficulty CSVs not present yet")
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try:
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