Benchmarking a Google Cloud GPU instance with Hashcat

GceGpuHashcat.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact_manual\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import re\n",
    "\n",
    "# Matplotlib configuration\n",
    "matplotlib.rc('lines', linewidth=2.0)\n",
    "matplotlib.rc('text', hinting_factor=12)\n",
    "matplotlib.rc('axes', facecolor='eeeeee', edgecolor='bcbcbc', grid=True,\n",
    "              labelsize='large')\n",
    "matplotlib.rc('legend', fancybox=True)\n",
    "matplotlib.rc('figure', figsize=(15,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# File to load hashcat --benchmark results from.\n",
    "BENCHMARK_FILEPATH = '/path/to/file/with/hashcat/benchmark/output.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Set up some constants\n",
    "LOWER_CASE = 'abcdefghijklmnopqrstuvwxyz'\n",
    "UPPER_CASE = LOWER_CASE.upper()\n",
    "NUMBERS = '0123456789'\n",
    "SYMBOLS = '!@#$%^&*()-_<>?,./:\";\\\\\\'+=|`~'\n",
    "\n",
    "COST_PER_HOUR = (0.70 * 8) + 0.10  # Cost per GPU * 8 GPUs + VM cost ($)\n",
    "\n",
    "UNIT_CONVERSIONS = {\n",
    "    '': 1,\n",
    "    'k': 1000,\n",
    "    'M': 1000000,\n",
    "    'G': 1000000000,\n",
    "    'T': 1000000000000,\n",
    "    'P': 1000000000000000,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Read the benchmark results and parse out a dict of hash type to hashes per second.\n",
    "benchmark_results = dict()\n",
    "hashtype_re = re.compile(r'^Hashtype: (.+)$')\n",
    "speed_re = re.compile(r'^Speed\\.Dev\\.#\\*\\.+: (.+H/s)')\n",
    "with open(BENCHMARK_FILEPATH) as f:\n",
    "    hashtype = ''\n",
    "    for line in f:\n",
    "        if hashtype == '':\n",
    "            match = hashtype_re.search(line)\n",
    "            hashtype = match.group(1) if match else ''\n",
    "        else:\n",
    "            match = speed_re.search(line)\n",
    "            if match:\n",
    "                (speed, units) = match.group(1).strip().split(' ')\n",
    "                units = units.replace('H/s', '')\n",
    "                benchmark_results[hashtype] = float(speed) * UNIT_CONVERSIONS[units]\n",
    "                hashtype = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Helper functions for determining compute time with various password parameters and hash rates.\n",
    "def seconds_to_compute(number_of_symbols, password_length, hashes_per_second):\n",
    "    combinations = number_of_symbols ** password_length\n",
    "    return float(combinations) / hashes_per_second\n",
    "\n",
    "def minutes_to_compute(number_of_symbols, password_length, hashes_per_second):\n",
    "    return seconds_to_compute(number_of_symbols, password_length, hashes_per_second) / 60\n",
    "\n",
    "def hours_to_compute(number_of_symbols, password_length, hashes_per_second):\n",
    "    return minutes_to_compute(number_of_symbols, password_length, hashes_per_second) / 60\n",
    "\n",
    "def days_to_compute(number_of_symbols, password_length, hashes_per_second):\n",
    "    return hours_to_compute(number_of_symbols, password_length, hashes_per_second) / 34\n",
    "\n",
    "def weeks_to_compute(number_of_symbols, password_length, hashes_per_second):\n",
    "    return days_to_compute(number_of_symbols, password_length, hashes_per_second) / 7\n",
    "\n",
    "def years_to_compute(number_of_symbols, password_length, hashes_per_second):\n",
    "    return weeks_to_compute(number_of_symbols, password_length, hashes_per_second) / 52\n",
    "\n",
    "# Map of Y-Axis labels to the functions to compute them.\n",
    "y_axis_funcs = {\n",
    "    'Seconds': seconds_to_compute,\n",
    "    'Minutes': minutes_to_compute,\n",
    "    'Hours': hours_to_compute,\n",
    "    'Days': days_to_compute,\n",
    "    'Weeks': weeks_to_compute,\n",
    "    'Years': years_to_compute,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Generate a plot of password length vs time to compute based on the benchmark results.\n",
    "def plt_time(algorithms_to_show,\n",
    "             min_password_length,\n",
    "             max_password_length,\n",
    "             number_of_symbols,\n",
    "             time_units,\n",
    "             benchmark_results):\n",
    "    plt.figure(1)\n",
    "    plt.title('Time to Compute Password Hashes by Password Length and Algorithm')\n",
    "    plt.xlabel('Password Length')\n",
    "    plt.ylabel(time_units)\n",
    "    plt.yscale('log')\n",
    "    # Disable scientific notation on the Y-Axis.\n",
    "    plt.gca().get_yaxis().set_major_formatter(FuncFormatter(lambda x, p: format(x, ',')))\n",
    "    colors = iter(cm.rainbow(np.linspace(0, 1, len(algorithms_to_show))))\n",
    "    for algorithm in algorithms_to_show:\n",
    "        x = range(min_password_length, max_password_length + 1)\n",
    "        y = [y_axis_funcs[time_units](number_of_symbols, password_length, benchmark_results[algorithm])\n",
    "             for password_length in x]\n",
    "        plt.scatter(x, y, color=next(colors), s=50, alpha=0.7, label=algorithm)\n",
    "    plt.legend(loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Generate a plot of password length vs cost to compute based on benchmark results and GCE pricing.\n",
    "def plt_cost(algorithms_to_show,\n",
    "             min_password_length,\n",
    "             max_password_length,\n",
    "             number_of_symbols,\n",
    "             benchmark_results):\n",
    "    plt.figure(2)\n",
    "    plt.title('Cost to Compute Password Hashes by Password Length and Algorithm')\n",
    "    plt.xlabel('Password Length')\n",
    "    plt.ylabel('Cost ($)')\n",
    "    plt.yscale('log')\n",
    "    # Disable scientific notation on the Y-Axis.\n",
    "    plt.gca().get_yaxis().set_major_formatter(FuncFormatter(lambda x, p: format(x, ',.0f')))\n",
    "    colors = iter(cm.rainbow(np.linspace(0, 1, len(algorithms_to_show))))\n",
    "    for algorithm in algorithms_to_show:\n",
    "        x = range(min_password_length, max_password_length + 1)\n",
    "        y = [hours_to_compute(number_of_symbols, password_length, benchmark_results[algorithm]) * COST_PER_HOUR\n",
    "             for password_length in x]\n",
    "        plt.scatter(x, y, color=next(colors), s=50, alpha=0.7, label=algorithm)\n",
    "    plt.legend(loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Widgets for adjusting the parameters of the equation.\n",
    "password_length = widgets.IntRangeSlider(\n",
    "    value=[6, 10],\n",
    "    min=1,\n",
    "    max=20,\n",
    "    description='Password Length:',\n",
    "    continuous_update=True,\n",
    ")\n",
    "\n",
    "algorithms = list(benchmark_results.keys())\n",
    "algorithms.sort()\n",
    "algorithms_to_show = widgets.SelectMultiple(\n",
    "    options=algorithms,\n",
    "    # By default show the first 5 algorithms.\n",
    "    value=algorithms[:5],\n",
    "    description='Algorithms to Show:',\n",
    ")\n",
    "\n",
    "time_units = widgets.ToggleButtons(\n",
    "    options=['Seconds', 'Minutes', 'Hours', 'Days', 'Weeks', 'Years'],\n",
    "    value='Hours',\n",
    "    description='Time Unit:',\n",
    ")\n",
    "\n",
    "use_lower_case = widgets.ToggleButton(\n",
    "    value=True,\n",
    "    description='Lower Case',\n",
    ")\n",
    "use_upper_case = widgets.ToggleButton(\n",
    "    value=True,\n",
    "    description='Upper Case',\n",
    ")\n",
    "use_numbers = widgets.ToggleButton(\n",
    "    value=True,\n",
    "    description='Numbers',\n",
    ")\n",
    "use_symbols = widgets.ToggleButton(\n",
    "    value=True,\n",
    "    description='Symbols',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [
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LiB1xfioIAusCBw1SglHrBilQ4IL0XYXlq7dz585kZ2fToUMHRo8eTWbmzwvC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f33d253f7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C4ZS0gxlOrVNCLbVqXS7ctzzEt8UQJpgC0TAJbuteSscmsbe7efNm0tLSqF+/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f33d250b470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the widgets and connect them to a function that will regenerate the plots.\n",
    "# Manual interaction is needed because generating the plots is too slow for interactivity.\n",
    "@interact_manual(password_length=password_length,\n",
    "                 algorithms_to_show=algorithms_to_show,\n",
    "                 time_units=time_units,\n",
    "                 use_lower_case=use_lower_case,\n",
    "                 use_upper_case=use_upper_case,\n",
    "                 use_numbers=use_numbers,\n",
    "                 use_symbols=use_symbols)\n",
    "def run_interactive_plots(password_length,\n",
    "                          algorithms_to_show,\n",
    "                          time_units,\n",
    "                          use_lower_case,\n",
    "                          use_upper_case,\n",
    "                          use_numbers,\n",
    "                          use_symbols):\n",
    "    min_password_length = password_length[0]\n",
    "    max_password_length = password_length[1]\n",
    "    # Add up the number of possible symbols for each password character.\n",
    "    number_of_symbols = 0\n",
    "    if use_lower_case:\n",
    "        number_of_symbols += len(LOWER_CASE)\n",
    "    if use_upper_case:\n",
    "        number_of_symbols += len(UPPER_CASE)\n",
    "    if use_numbers:\n",
    "        number_of_symbols += len(NUMBERS)\n",
    "    if use_symbols:\n",
    "        number_of_symbols += len(SYMBOLS)\n",
    "    plt_time(algorithms_to_show,\n",
    "             min_password_length,\n",
    "             max_password_length,\n",
    "             number_of_symbols,\n",
    "             time_units,\n",
    "             benchmark_results)\n",
    "    plt_cost(algorithms_to_show,\n",
    "             min_password_length,\n",
    "             max_password_length,\n",
    "             number_of_symbols,\n",
    "             benchmark_results)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.2"
  },
  "widgets": {
   "state": {
    "4876ccfdddde4a06a32e0f43867e7c3d": {
     "views": [
      {
       "cell_index": 8
      }
     ]
    }
   },
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}