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diff --git a/analysis/countermeasures/visualize.ipynb b/analysis/countermeasures/visualize.ipynb new file mode 100644 index 0000000..709e566 --- /dev/null +++ b/analysis/countermeasures/visualize.ipynb @@ -0,0 +1,406 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "52a95c74-8fc0-4021-a8e9-8587ff6f1d9e", + "metadata": {}, + "source": [ + "# Visualizing prob-maps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3232df80-2a65-47ce-bc77-6a64f44d2404", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import itertools\n", + "import glob\n", + "import gc\n", + "\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from tqdm.auto import tqdm, trange\n", + "from statsmodels.stats.proportion import proportion_confint\n", + "\n", + "from pyecsca.ec.mult import *\n", + "from pyecsca.misc.utils import TaskExecutor\n", + "\n", + "from common import *\n", + "\n", + "%matplotlib ipympl" + ] + }, + { + "cell_type": "markdown", + "id": "4273bd5e-0ec6-4e5c-b63e-74cc325a8ece", + "metadata": {}, + "source": [ + "## Setup\n", + "Setup some plotting and the computations of prob-maps out of the small scalar data and divisors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e89e66dc-4a9b-4320-8612-a8fa9af04b69", + "metadata": {}, + "outputs": [], + "source": [ + "# Setup the ticks and colors deterministically.\n", + "mult_klasses = sorted(list(set(map(lambda mult: mult.klass, all_mults))), key=lambda klass: klass.__name__)\n", + "mult_kwarg_map = {klass: 0 for klass in mult_klasses}\n", + "mult_cm_map = {mult: 0 for mult in all_mults}\n", + "mult_colors = matplotlib.cm.tab20(range(len(mult_klasses)))\n", + "mult_styles = ['-', '--', '-.', ':', (5, (10, 3)), (0, (5, 1)), (0, (3, 1, 1, 1, 1, 1)), (0, (3, 1, 1, 1)), (0, (1, 1)), (0, (3, 10, 1, 10))]\n", + "mult_markers = [None, \"o\", \"+\", \"*\", \"^\", \"s\"]\n", + "colors = {}\n", + "styles = {}\n", + "markers = {}\n", + "for mult in all_mults:\n", + " color = mult_colors[mult_klasses.index(mult.klass) % len(mult_colors)]\n", + " style = mult_styles[mult_kwarg_map[mult.klass] % len(mult_styles)]\n", + " mult_kwarg_map[mult.klass] += 1\n", + " for cm in (None, \"gsr\", \"additive\", \"multiplicative\", \"euclidean\", \"bt\"):\n", + " mwc = mult.with_countermeasure(cm)\n", + " colors[mwc] = color\n", + " styles[mwc] = style\n", + " markers[mwc] = mult_markers[mult_cm_map[mult] % len(mult_markers)]\n", + " mult_cm_map[mult] += 1\n", + "\n", + "majticks = np.arange(0, 1, 0.1)\n", + "minticks = np.arange(0, 1, 0.05)" + ] + }, + { + "cell_type": "markdown", + "id": "2596562f-8a6a-4a25-ae82-a6b9562d8a40", + "metadata": {}, + "source": [ + "## Divisors\n", + "The cell below contains some interesting divisors for distinguishing scalarmults." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bab2a086-8b3d-4e76-bf5c-46ea2b617708", + "metadata": {}, + "outputs": [], + "source": [ + "from common import divisor_map\n", + "for d, ds in divisor_map.items():\n", + " print(f\"{d:<27}\", ds[:3], \"...\", ds[-1:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "638f8634-1f6e-4844-a796-096611dfbac2", + "metadata": {}, + "outputs": [], + "source": [ + "bits = 256\n", + "num_workers = 28" + ] + }, + { + "cell_type": "markdown", + "id": "8b008248-a0aa-41fa-933c-f325f8eec31b", + "metadata": {}, + "source": [ + "## Configuration\n", + "Select the mults you want to compute the prob-maps for here as well as a set of divisors. It is good to set `all` here, compute the prob-maps for all the divisors, save them and they continue with visualizing them on subsets of divisors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d2a0f19-8275-4db8-b3fc-c930d8ba2177", + "metadata": {}, + "outputs": [], + "source": [ + "selected_mults = all_mults\n", + "divisor_name = \"all\"\n", + "kind = \"precomp+necessary\"\n", + "showci = False\n", + "selected_divisors = divisor_map[divisor_name]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19d986ab-5fe7-4dd6-b5b5-4e75307217d6", + "metadata": {}, + "outputs": [], + "source": [ + "# Optionally, load\n", + "with open(f\"{divisor_name}_{kind}_distrs.pickle\", \"rb\") as f:\n", + " distributions_mults = pickle.load(f)" + ] + }, + { + "cell_type": "markdown", + "id": "ef5b7a43-74b4-4e72-a3a1-955e175f5297", + "metadata": {}, + "source": [ + "Now, go over all the divisor sets and visualize them (without the combs) into PNGs in the graphs/ directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ccc28f6-3994-4a0d-8639-2f6df4dddd26", + "metadata": {}, + "outputs": [], + "source": [ + "for mult, probmap in distributions_mults.items():\n", + " for divisor in sorted(divisor_map[divisor_name]):\n", + " if divisor not in probmap.probs:\n", + " print(f\"Missing {mult}, {divisor}\")\n", + " if probmap.kind is not None and probmap.kind != kind:\n", + " print(\"Bad kind! Did you forget to load?\")" + ] + }, + { + "cell_type": "markdown", + "id": "9b6f169b-07b3-4b27-ba36-8b90418cd072", + "metadata": {}, + "source": [ + "## Plots (nocomb)\n", + "Let's visualize all the divisor groups while looking at the multipliers and countermeasures except the comb-like ones." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "906b5d78-b3a4-4cbb-8051-092d411ba735", + "metadata": {}, + "outputs": [], + "source": [ + "for divisor_name in divisor_map:\n", + " plot_mults = list(filter(lambda mult: mult in distributions_mults and mult.klass not in (CombMultiplier, BGMWMultiplier), all_mults_with_ctr))\n", + " print(divisor_name, \"nocomb\")\n", + " plot_divisors = sorted(divisor_map[divisor_name])\n", + " L = len(plot_divisors)\n", + " N = len(plot_mults)\n", + " x = list(range(L))\n", + " \n", + " fig = plt.figure(figsize=(L/4+10, 24))\n", + " ax = plt.subplot(111)\n", + " \n", + " vals = np.zeros((N, L))\n", + " n_samples = 0\n", + " for i, mult in enumerate(plot_mults):\n", + " probmap = distributions_mults[mult]\n", + " y_values = [probmap[l] for l in plot_divisors]\n", + " vals[i,] = y_values\n", + " ax.plot(x, y_values,\n", + " color=colors[mult],\n", + " linestyle=styles[mult],\n", + " marker=markers[mult],\n", + " label=str(mult) if mult.countermeasure is None else \"_nolegend_\")\n", + " if showci:\n", + " cis = [conf_interval(p, probmap.samples) for p in y_values]\n", + " ci_low = [ci[0] for ci in cis]\n", + " ci_high = [ci[1] for ci in cis]\n", + " ax.fill_between(x, ci_low, ci_high, color=\"black\", alpha=0.1)\n", + " n_samples += probmap.samples\n", + " \n", + " ax.set_title(f\"{divisor_name} ({kind})\\nSamples: \" + str(n_samples//N))\n", + " \n", + " #var = np.var(vals, axis=0)\n", + " #ax.plot(x, var / np.max(var), label=\"cross-mult variance (normalized)\", ls=\"--\", lw=2, color=\"black\")\n", + " \n", + " ax.set_xlabel(\"divisors\")\n", + " ax.set_ylabel(\"error probability\")\n", + " ax.set_yticks(majticks)\n", + " ax.set_yticks(minticks, minor=True)\n", + " ax.set_xticks(x, plot_divisors, rotation=90)\n", + " \n", + " ax.grid(axis=\"y\", which=\"major\", alpha=0.7)\n", + " ax.grid(axis=\"y\", which=\"minor\", alpha=0.3)\n", + " ax.grid(axis=\"x\", alpha=0.7)\n", + " plt.tight_layout()\n", + " box = ax.get_position()\n", + " ax.set_position([box.x0, box.y0, box.width * 0.9, box.height])\n", + " \n", + " ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + "\n", + " fig.savefig(f\"graphs/{kind}-kind/{divisor_name}-nocomb{'+ci' if showci else ''}.pdf\");\n", + " plt.close()" + ] + }, + { + "cell_type": "markdown", + "id": "4068e7d0-addb-45d0-ba87-e572d4c82fbd", + "metadata": {}, + "source": [ + "## Plots (allmults)\n", + "Now, lets also do plots with allmults for all divisor groups." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b9aa7a8-0d9d-4ce3-a936-8ced2948f562", + "metadata": {}, + "outputs": [], + "source": [ + "for divisor_name in divisor_map:\n", + " plot_mults = list(filter(lambda mult: mult in distributions_mults, all_mults_with_ctr))\n", + " print(divisor_name, \"allmults\")\n", + " plot_divisors = sorted(divisor_map[divisor_name])\n", + " L = len(plot_divisors)\n", + " N = len(plot_mults)\n", + " x = list(range(L))\n", + " \n", + " fig = plt.figure(figsize=(L/4+10, 26))\n", + " ax = plt.subplot(111)\n", + " \n", + " vals = np.zeros((N, L))\n", + " n_samples = 0\n", + " for i, mult in enumerate(plot_mults):\n", + " probmap = distributions_mults[mult]\n", + " y_values = [probmap[l] for l in plot_divisors]\n", + " vals[i,] = y_values\n", + " ax.plot(x, y_values,\n", + " color=colors[mult],\n", + " linestyle=styles[mult],\n", + " marker=markers[mult],\n", + " label=str(mult) if mult.countermeasure is None else \"_nolegend_\")\n", + " if showci:\n", + " cis = [conf_interval(p, probmap.samples) for p in y_values]\n", + " ci_low = [ci[0] for ci in cis]\n", + " ci_high = [ci[1] for ci in cis]\n", + " ax.fill_between(x, ci_low, ci_high, color=\"black\", alpha=0.1)\n", + " n_samples += probmap.samples\n", + " \n", + " ax.set_title(f\"{divisor_name} ({kind})\\nSamples(avg): \" + str(n_samples//N))\n", + " \n", + " #var = np.var(vals, axis=0)\n", + " #ax.plot(x, var / np.max(var), label=\"cross-mult variance (normalized)\", ls=\"--\", lw=2, color=\"black\")\n", + " \n", + " ax.set_xlabel(\"divisors\")\n", + " ax.set_ylabel(\"error probability\")\n", + " ax.set_yticks(majticks)\n", + " ax.set_yticks(minticks, minor=True)\n", + " ax.set_xticks(x, plot_divisors, rotation=90)\n", + " \n", + " ax.grid(axis=\"y\", which=\"major\", alpha=0.7)\n", + " ax.grid(axis=\"y\", which=\"minor\", alpha=0.3)\n", + " ax.grid(axis=\"x\", alpha=0.7)\n", + " plt.tight_layout()\n", + " box = ax.get_position()\n", + " ax.set_position([box.x0, box.y0, box.width * 0.9, box.height])\n", + " \n", + " ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + "\n", + " fig.savefig(f\"graphs/{kind}-kind/{divisor_name}-allmults{'+ci' if showci else ''}.pdf\")\n", + " plt.close()" + ] + }, + { + "cell_type": "markdown", + "id": "df2e236a-4540-4677-a7f1-563c4cc37a3e", + "metadata": {}, + "source": [ + "## Interactive plot\n", + "Below you can choose a concrete divisor set and visualize it with all the mults, or just some to your liking." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b464865d-b169-446e-a9e7-0cead699aee1", + "metadata": {}, + "outputs": [], + "source": [ + "#divisor_name = \"powers_of_2_large\"\n", + "divisor_name = \"feature\"\n", + "plot_mults = list(filter(lambda mult: mult in distributions_mults, all_mults_with_ctr))\n", + "#plot_divisors = (61, 65, 111, 165, 1536, 12288) \n", + "plot_divisors = (55, 65, 165, 248, 3072)\n", + "L = len(plot_divisors)\n", + "N = len(plot_mults)\n", + "x = list(range(L))\n", + "\n", + "fig = plt.figure(figsize=(L/4+15, 24))\n", + "ax = plt.subplot(111)\n", + "\n", + "vals = np.zeros((N, L))\n", + "n_samples = 0\n", + "for i, mult in enumerate(plot_mults):\n", + " probmap = distributions_mults[mult]\n", + " y_values = [probmap[l] for l in plot_divisors]\n", + " vals[i,] = y_values\n", + " ax.plot(x, y_values,\n", + " color=colors[mult],\n", + " linestyle=styles[mult],\n", + " marker=markers[mult],\n", + " label=str(mult) if mult.countermeasure is None else \"_nolegend_\")\n", + " if showci:\n", + " cis = [conf_interval(p, probmap.samples) for p in y_values]\n", + " ci_low = [ci[0] for ci in cis]\n", + " ci_high = [ci[1] for ci in cis]\n", + " ax.fill_between(x, ci_low, ci_high, color=\"black\", alpha=0.1)\n", + " n_samples += probmap.samples\n", + "\n", + "ax.set_title(f\"{divisor_name} ({kind})\\nSamples(avg): \" + str(n_samples//N))\n", + "\n", + "#var = np.var(vals, axis=0)\n", + "#ax.plot(x, var / np.max(var), label=\"cross-mult variance (normalized)\", ls=\"--\", lw=2, color=\"black\")\n", + "\n", + "ax.set_xlabel(\"divisors\")\n", + "ax.set_ylabel(\"error probability\")\n", + "ax.set_yticks(majticks)\n", + "ax.set_yticks(minticks, minor=True)\n", + "ax.set_xticks(x, plot_divisors, rotation=90)\n", + "\n", + "ax.grid(axis=\"y\", which=\"major\", alpha=0.7)\n", + "ax.grid(axis=\"y\", which=\"minor\", alpha=0.3)\n", + "ax.grid(axis=\"x\", alpha=0.7)\n", + "plt.tight_layout()\n", + "box = ax.get_position()\n", + "ax.set_position([box.x0, box.y0, box.width * 0.7, box.height])\n", + "\n", + "# Put a legend to the right of the current axis\n", + "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d68f0bfc-cdf1-4891-b0e5-0b6d1b02ded7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} |