Learned-key sensitivity, brute-force and real-token stages; legend order
exp_learned.py completes the learned side of the result stages, so every figure can carry both realizations of keyed masking. real() holds the structured artifacts aside and restores them, since exp_real_sec writes fixed file names. replot_security.py ranks legend handles from one declared order at all three ax.legend call sites, so entries no longer follow plot-call order and drift between figures.
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KiHoLee
+51
-16
@@ -84,18 +84,17 @@ STY = {
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# fixed label dictionary: tables and prose copy these strings verbatim
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LBL = {
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"legit": "KM (structured)",
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"legit_learned": "KM (learned)",
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"legit": "KM (str.)",
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"legit_learned": "KM (lrn.)",
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"oma": "OMA",
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"eve_pub": "Eavesdropper, public masks",
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"eve_key": "Eavesdropper", # the wrong-key condition is in the caption
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"chance": "Random guess",
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"nojam": "No jammer",
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"mask": "KM (structured)",
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"mask": "KM (str.)",
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"perm": "Permutation key",
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"pad": "Index cipher",
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"insider": "Insider",
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"legit_ref": "Legitimate rate",
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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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@@ -223,6 +222,29 @@ def main_legit(snr_db="10"):
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raise KeyError("no %s dB row in sec_snr.csv" % snr_db)
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# Legend order, applied by place_legend to whatever subset a figure
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# draws: the proposal first, then the comparison schemes in the order of
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# Table IV, then adversaries, then reference levels. Entries not listed
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# keep their plot order after the ranked ones.
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LEGEND_ORDER = [
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"KM (str.)", "KM (lrn.)",
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"Public masks", "Permutation key", "Index cipher", "OMA",
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"Eavesdropper", "Eavesdropper, keyed", "Eavesdropper, public masks",
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"Outsider", "Insider",
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"No jammer", "Random guess",
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]
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def _rank(label):
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"""Rank a legend label, matching the collection-SNR variants of
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Fig. 7 on their scheme prefix so they stay together and in order."""
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for i, name in enumerate(LEGEND_ORDER):
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if label == name or label.startswith(name + ","):
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return i
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return len(LEGEND_ORDER)
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def place_legend(ax, cands=("lower left", "upper left", "center left",
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"center right", "lower center", "upper right",
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"upper center", "center", "lower right"),
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@@ -247,7 +269,11 @@ def place_legend(ax, cands=("lower left", "upper left", "center left",
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best = None
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for size in sizes:
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for loc in cands:
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leg = ax.legend(loc=loc, prop={"size": size}, ncol=ncol,
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h, l = ax.get_legend_handles_labels()
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idx = sorted(range(len(l)), key=lambda k: (_rank(l[k]), k))
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h = [h[k] for k in idx]
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l = [l[k] for k in idx]
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leg = ax.legend(h, l, loc=loc, prop={"size": size}, ncol=ncol,
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handlelength=1.4, columnspacing=0.9,
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handletextpad=0.5, borderaxespad=0.55,
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framealpha=1.0)
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@@ -270,13 +296,21 @@ def place_legend(ax, cands=("lower left", "upper left", "center left",
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if best is None or hits < best[2]:
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best = (loc, size, hits)
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if hits == 0:
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ax.legend(loc=loc, prop={"size": size}, ncol=ncol,
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h, l = ax.get_legend_handles_labels()
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idx = sorted(range(len(l)), key=lambda k: (_rank(l[k]), k))
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h = [h[k] for k in idx]
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l = [l[k] for k in idx]
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ax.legend(h, l, loc=loc, prop={"size": size}, ncol=ncol,
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handlelength=1.4, columnspacing=0.9,
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handletextpad=0.5, borderaxespad=0.55,
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framealpha=1.0)
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PL_CHOSEN.append(size)
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return best
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ax.legend(loc=best[0], prop={"size": best[1]}, ncol=ncol,
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h, l = ax.get_legend_handles_labels()
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idx = sorted(range(len(l)), key=lambda k: (_rank(l[k]), k))
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h = [h[k] for k in idx]
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l = [l[k] for k in idx]
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ax.legend(h, l, loc=best[0], prop={"size": best[1]}, ncol=ncol,
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handlelength=1.4, columnspacing=0.9, handletextpad=0.5,
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borderaxespad=0.55, framealpha=1.0)
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PL_CHOSEN.append(best[1])
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@@ -390,6 +424,9 @@ def fig_sens():
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fig, ax = plt.subplots()
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ax.plot(x, col(r, "ser_mask"), **STY["km_str"],
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markevery=(0, 3), label=LBL["mask"], **UNDER)
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rs = load("sec_sens_learned.csv")
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ax.plot(col(rs, "frac"), col(rs, "ser_mask"), **STY["km_lrn"],
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markevery=(1, 3), label=LBL["legit_learned"])
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ax.plot(x, col(r, "ser_perm"), **STY["perm"],
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markevery=(1, 3), label=LBL["perm"], **OVER)
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ax.plot(x, col(r, "ser_pad"), **STY["pad"],
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@@ -398,9 +435,6 @@ def fig_sens():
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# copy of the configuration constants
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chance = float(load("sec_snr.csv")[0]["chance"])
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ax.axhline(chance, color=C_CH, ls=":", lw=0.9, label=LBL["chance"])
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# the narration reads these curves against the legitimate rate
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ax.axhline(main_legit(), color=C_OMA, ls=(0, (4, 2)), lw=0.9,
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label=LBL["legit_ref"])
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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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@@ -420,9 +454,9 @@ def fig_brute():
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markevery=(1, 3), label=LBL["pad"], **OVER)
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ax.semilogx(x, col(r, "ser_mask"), **STY["km_str"],
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markevery=(2, 3), label=LBL["mask"])
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legit = main_legit()
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ax.axhline(legit, color=C_OMA, ls=(0, (4, 2)), lw=0.9,
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label=LBL["legit_ref"])
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rb = load("sec_brute_learned.csv")
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ax.semilogx(col(rb, "K"), col(rb, "ser_mask"), **STY["km_lrn"],
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markevery=(1, 3), label=LBL["legit_learned"])
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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(0.0, 1.05) # keep the reference line off the spine
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@@ -438,6 +472,10 @@ def fig_real():
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# legitimate and OMA curves are separate at this frame
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ax.semilogy(x, col(r, "ter_legit"), **STY["km_str"],
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markevery=(0, 2), label=LBL["legit"], **UNDER)
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rt = load("real_sec_ter_learned.csv")
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ax.semilogy(col(rt, "snr_db"), col(rt, "ter_legit"),
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**STY["km_lrn"], markevery=(1, 2),
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label=LBL["legit_learned"])
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ax.semilogy(x, col(r, "ter_oma"), **STY["oma"],
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markevery=(1, 2), label=LBL["oma"], **OVER)
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ax.semilogy(x, col(r, "ter_insider"), **STY["insider"],
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@@ -484,9 +522,6 @@ def fig_kpa():
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# eavesdropper curves, namely the four-user average of eval_ser_sse
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# in the main configuration, rather than the user-1 convention of the
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# scheme-comparison table
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legit = main_legit()
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ax.axhline(legit, color=C_OMA, ls=(0, (4, 2)), lw=0.9,
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label=LBL["legit_ref"])
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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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