Merge pull request #2 from andrr3j/DPA

DPA notebook

1 files changed, 264 insertions, 0 deletions

diff --git a/DPA.ipynb b/DPA.ipynb
new file mode 100644
index 0000000..382cacc
--- /dev/null
+++ b/DPA.ipynb
@@ -0,0 +1,264 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Differential power analysis on pyecsca emulated traces"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyecsca.ec.mult import LTRMultiplier\n",
+    "from pyecsca.ec.mod import Mod\n",
+    "from pyecsca.ec.point import Point, InfinityPoint\n",
+    "from pyecsca.ec.model import ShortWeierstrassModel\n",
+    "from pyecsca.ec.curve import EllipticCurve\n",
+    "from pyecsca.ec.params import DomainParameters\n",
+    "from pyecsca.sca.attack.DPA import DPA\n",
+    "from pyecsca.sca.attack.leakage_model import HammingWeight\n",
+    "from pyecsca.sca.target.emulator import EmulatorTarget\n",
+    "from random import randint\n",
+    "import holoviews as hv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hv.extension(\"bokeh\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ShortWeierstrassModel()\n",
+    "coords = model.coordinates[\"projective\"]\n",
+    "p = 0xd7d1247f\n",
+    "a = Mod(0xa4a44016, p)\n",
+    "b = Mod(0x73f76716, p)\n",
+    "n = 0xd7d2a475\n",
+    "h = 1\n",
+    "gx, gy, gz = Mod(0x54eed6d7, p), Mod(0x6f1e55ac, p), Mod(1, p)\n",
+    "generator = Point(coords, X=gx, Y=gy, Z=gz)\n",
+    "neutral = InfinityPoint(coords)\n",
+    "\n",
+    "curve = EllipticCurve(model, coords, p, neutral, {\"a\": a, \"b\": b})\n",
+    "params = DomainParameters(curve, generator, n, h)\n",
+    "\n",
+    "add = coords.formulas[\"add-2015-rcb\"]\n",
+    "dbl = coords.formulas[\"dbl-2015-rcb\"]\n",
+    "scl = coords.formulas[\"z\"]\n",
+    "\n",
+    "mult = LTRMultiplier(add, dbl, None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scalar_bit_length = 8\n",
+    "secret_scalar = randint(128, 255)\n",
+    "print(secret_scalar)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Emulation of traces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_of_traces = 5000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emulator = EmulatorTarget(model, coords, mult)\n",
+    "emulator.set_params(params)\n",
+    "emulator.set_leakage_model(HammingWeight())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "points, traces = emulator.emulate_scalar_mult_traces(num_of_traces, secret_scalar)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DPA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mult.init(params, params.generator)\n",
+    "real_pub_key = mult.multiply(secret_scalar)\n",
+    "dpa = DPA(points, traces, mult, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Recover the 8-bit scalar bit by bit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recovered_scalar = 128 #1000 0000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recovered_scalar = dpa.recover_bit(recovered_scalar, 1, scalar_bit_length, real_pub_key)\n",
+    "print(f\"Recovered scalar after recovering 2. bit of the secret scalar: {recovered_scalar} = {recovered_scalar:b}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Difference of means when guessing 2. bit (guessed scalar 192 = 1100 0000)\n",
+    "dpa.plot_difference_of_means(dpa.doms['guess_one'][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Difference of means when guessing 2. bit (guessed scalar 128 = 1000 0000)\n",
+    "dpa.plot_difference_of_means(dpa.doms['guess_zero'][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We can do this for each bit of the scalar\n",
+    "for i in range(2, scalar_bit_length):\n",
+    "    recovered_scalar = dpa.recover_bit(recovered_scalar, i, scalar_bit_length, real_pub_key)\n",
+    "    print(f\"Recovered scalar after recovering {i + 1}. bit of the secret scalar: {recovered_scalar} = {recovered_scalar:b}\")\n",
+    "print(recovered_scalar == secret_scalar)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Recover the whole 8-bit scalar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recovered_scalar = dpa.perform(scalar_bit_length, real_pub_key)\n",
+    "print(recovered_scalar)\n",
+    "print(recovered_scalar == secret_scalar)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### We can look at the difference of means after last recovered bit for both zero guess and one guess"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Zero guess\n",
+    "dpa.plot_difference_of_means(dpa.doms['guess_zero'][-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# One guess\n",
+    "dpa.plot_difference_of_means(dpa.doms['guess_one'][-1])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.9.16"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}