KiHoLee
3d5a7fc6f3
Unconstrained key training converged to disjoint sparse supports: 99 percent of each users key energy sat on three or four of the sixteen entries, with pairwise disjoint supports and one numerically dead codebook column. That is an orthogonal slot allocation, so the superposition collapsed into OMA and the key space was far smaller than the dense direction the brute-force study assumes. The main configuration is now the structured Walsh-Hadamard family, which is dense, exactly orthogonal, unit modulus, and already the best family in the key-family table. base_keys generalizes to any key length by truncating the next power-of-two Sylvester order, and the key-length sweep keeps only lengths where the truncated rows stay exactly orthogonal, verified numerically. Also fixes the M-PAM energy normalization in oma_ser_keylen, which used sqrt(6g/(M^2-1)) where unit average symbol energy gives A^2=3/(M^2-1); the closed form was 3 dB optimistic and now reproduces a direct Monte Carlo to 1e-5. Results move accordingly: the proposal now stays below OMA at every SNR and reaches 1.52x at key length 64, while the jamming margin falls to 5.5-6.3 dB and the brute-force curve to 0.59 at a million guesses.
337 lines
13 KiB
Python
337 lines
13 KiB
Python
"""Canonical replot script for paper 11: regenerates every result figure
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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 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 : 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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Curves that coincide by construction are drawn deliberately layered: the
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lower one wide and semi-transparent, the upper one narrow with open
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markers, and their markers staggered to different sample points through
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markevery offsets. Marker size is uniform across every figure, so the
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stagger, not the size, is what keeps each legend entry visible.
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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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import matplotlib.pyplot as plt
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ROOT = Path(__file__).resolve().parents[1]
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DATA = ROOT / "data"
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FIG = ROOT / "fig"
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FIG.mkdir(exist_ok=True)
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plt.rcParams.update({
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"font.family": "serif",
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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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"xtick.labelsize": 8,
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"ytick.labelsize": 8,
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"axes.grid": True,
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"grid.linestyle": "--",
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"grid.linewidth": 0.4,
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"grid.alpha": 0.6,
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"lines.linewidth": 1.3,
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"lines.markersize": 4.5,
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"figure.figsize": (3.15, 2.36),
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"pdf.fonttype": 42,
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})
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AXES_RECT = dict(left=0.185, right=0.965, top=0.955, bottom=0.195)
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C_LEGIT = "#c0392b"
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C_EVE = "#2c5fa8"
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C_OMA = "#7f8c8d"
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C_CH = "#95a5a6"
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C_MATCH = "#8e44ad"
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C_PUB = "#16a085"
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# fixed label dictionary: tables and prose copy these strings verbatim
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LBL = {
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"legit": "Legitimate",
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"oma": "OMA",
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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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"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, alpha=0.85) # thick filled line, layered under
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# and for the curve riding on top of it
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OVER = dict(lw=1.2, mfc="none") # thin open marker, rides on top
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def load(name):
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with open(DATA / name) as f:
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return list(csv.DictReader(f))
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def col(rows, k, f=float):
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return [f(r[k]) for r in rows]
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def save(fig, name):
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"""Write the figure and assert that no axis label is clipped.
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A long y label, or wide minor tick labels such as 6x10^-1 on a log
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axis that spans less than a decade, silently pushes the label off
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the canvas under the fixed axes rectangle. Reading the plotting code
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cannot reveal this, so the check is made on the rendered geometry.
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"""
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fig.subplots_adjust(**AXES_RECT)
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fig.canvas.draw()
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fbox = fig.get_window_extent()
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for ax in fig.axes:
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for lbl in (ax.yaxis.label, ax.xaxis.label):
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if not lbl.get_text():
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continue
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b = lbl.get_window_extent()
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if (b.x0 < fbox.x0 or b.y0 < fbox.y0
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or b.x1 > fbox.x1 or b.y1 > fbox.y1):
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raise RuntimeError(
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f"{name}: axis label '{lbl.get_text()}' is clipped "
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f"(label {b} outside figure {fbox}); shorten the "
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f"label or widen the margin")
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# No curve may pass under the legend box. Reading the code cannot
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# reveal this, so the check is made on the rendered geometry, the
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# same discipline as the clipping guard above.
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leg = ax.get_legend()
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if leg is not None:
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lb = leg.get_window_extent()
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for line in ax.get_lines():
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# full-span reference lines (axhline/axvline) carry axes-
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# fraction endpoints [0,1]; they are not data curves and,
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# spanning the whole axis, would forbid any bottom legend
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xd = list(line.get_xdata())
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if xd == [0, 1] or list(line.get_ydata()) == [0, 1]:
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continue
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xy = line.get_xydata()
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if len(xy) == 0:
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continue
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for px, py in ax.transData.transform(xy):
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if lb.x0 <= px <= lb.x1 and lb.y0 <= py <= lb.y1:
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raise RuntimeError(
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f"{name}: a data curve passes under the legend "
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f"box; move the legend or shrink it")
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fig.savefig(FIG / f"{name}.pdf")
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plt.close(fig)
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print("[OK]", name)
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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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markevery=(0, 3), label=LBL["legit"], **UNDER)
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ax.semilogy(x, col(r, "oma"), color=C_OMA, marker="^", ls=":",
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markevery=(1, 3), 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", markevery=(2, 3), markerfacecolor="none",
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label=LBL["eve_pub"])
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ax.semilogy(x, col(r, "eve_wrong"), color=C_EVE, marker="s", ls="--",
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label=LBL["eve_key"])
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ax.plot(x, col(r, "chance"), color=C_CH, ls="-.", lw=0.9,
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label=LBL["chance"])
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ax.set_xlabel("SNR (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.legend(loc="lower left")
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save(fig, "fig_sec_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 below L=16 unless 16/L is 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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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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ax.set_ylabel("SER")
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ax.set_xscale("log", base=2)
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# the curves sweep the upper-left to lower-right diagonal, leaving the
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# lower-left corner empty
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ax.legend(loc="lower left")
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save(fig, "fig_sec_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. 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, "matched"), color=C_MATCH, marker="P", ls="--",
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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 0.002; deliberate layering
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ax.plot(x, col(r, "blind"), color=C_LEGIT, marker="o", ls="-",
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markevery=(0, 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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markevery=(me // 2, 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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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="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. 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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markevery=(0, 3), 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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markevery=(1, 3), 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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markevery=(2, 3), lw=1.2, 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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ax.set_ylabel("Eavesdropper SER")
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ax.set_xlim(0, 1)
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ax.legend(loc="lower left")
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save(fig, "fig_sec_sens")
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def fig_brute():
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"""Brute-force search against the three keyed schemes at the same
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key length, each mapped through its own sensitivity curve."""
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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_perm"), color=C_EVE, marker="s", ls="--",
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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=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(0.2, 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_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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markevery=(0, 2), label=LBL["legit"], **UNDER)
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ax.semilogy(x, col(r, "ter_oma"), color=C_OMA, marker="^", ls=":",
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markevery=(1, 2), 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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markevery=(0, 2), lw=2.6, alpha=0.85, label=LBL["insider"])
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ax.semilogy(x, col(r, "ter_eve"), color=C_EVE, marker="s", ls="--",
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markevery=(1, 2), 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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ax.legend(loc="lower left")
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save(fig, "fig_sec_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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20.0: ("#16a085", "v")}
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for snr, (c, mk) in sty.items():
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rows = [row for row in r if float(row["snr_db"]) == snr]
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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=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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# convention of the scheme-comparison table
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kl = load("sec_keylen.csv")
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legit = float([r for r in kl if int(r["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("Known-plaintext frames $N$")
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ax.set_ylabel("Eavesdropper SER")
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ax.set_xscale("log", base=2)
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# the 0 dB curve sweeps the upper-right, so anchor the legend at the
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# top edge past the steep drops, above every curve at large N
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ax.set_ylim(top=1.18)
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ax.legend(loc="upper right", bbox_to_anchor=(1.0, 1.04))
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save(fig, "fig_sec_kpa")
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def main():
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fig_snr()
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fig_keylen()
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fig_jam()
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try:
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fig_sens()
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fig_brute()
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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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fig_real()
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except FileNotFoundError:
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print("[skip] real-token CSV not present yet")
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try:
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fig_kpa()
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except FileNotFoundError:
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print("[skip] known-plaintext CSV not present yet")
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print("[done] figures in", FIG)
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if __name__ == "__main__":
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main()
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