Examining Authentication Timing Side Channel Attacks with libc strcmp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "collect-knight",
   "metadata": {},
   "source": [
    "## Examining timing side-channel attacks\n",
    "\n",
    "The idea is to show that time analysis can reveal meaningful information about a string used in a secret or a password if the amount of time it takes to perform the string match isn't buffered. \n",
    "\n",
    "This example will be to show how the libc strcmp method reveals information about a secret string and, possibly, the string itself in a brute force attack. We'll be using python code to demonstrate the action and analysis, but this can be performed with any language that uses similar methods.\n",
    "\n",
    "\n",
    "Our secret string is 'apple'.\n",
    "\n",
    "A client presents the secret string to the server, who knows the secret string, in an operation that will fail if the secret is incorrect.\n",
    "\n",
    "So:\n",
    "\n",
    "Client -> 'apple' -> Server  \n",
    "\n",
    "Server: \n",
    "- receives a buffer containing the string. \n",
    "- gets a copy of the secret \n",
    "- strcmp's the two strings\n",
    "    - analyze bytes from l to r\n",
    "    - when a byte fails, return False\n",
    "    - if no bytes fail move to next char. \n",
    "    - Return True if nothing fails or False when a match fails.\n",
    "Server ->True/False -> Client \n",
    "\n",
    "The problem is that for every correct chunk of text on the LHS the \"fail\" takes longer to return. Also - longer strings also take longer to return.\n",
    "\n",
    "So \"b\" or \"broken\" returns a failure instantly, but \"a\", \"aplomb\", \"app\", \"application\", etc will each take slightly longer for each character matched. The times are tiny, but it's possible to model the jitter window such that these lengths don't get lost in the noise. This can drastically cut down the time of a brute force attack and enhance its chances of success.\n",
    "\n",
    "TL;DR - using sound hash functions which return in a constant time and not rolling-your-own solves this problem.\n",
    "\n",
    "Also: for authentication purposes it's an antipattern to store the secret plaintext or decode an encrypted secret to compare in plaintext. Hashing the secret, storing the hash then comparing the hashes also obscures the size of your secret by making the compared strings the same lengh, and increasing the problem for a brute force attack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "solved-arbitration",
   "metadata": {},
   "outputs": [],
   "source": [
    "# need to use attotime here to get fractional nanoseconds\n",
    "import sys, os, time, string, attotime, secrets, hashlib\n",
    "from datetime import datetime\n",
    "\n",
    "# imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Viz imports\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Config matplotlib\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"patch.force_edgecolor\"] = True # in matplotlib, edge borders are turned off by default.\n",
    "sns.set_style(\"darkgrid\") # set a grey grid as a background\n",
    "\n",
    "# turn off warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "seeing-prophet",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we only care about urlsafe characters - alpha, numeric, dash and underscore. Omitting . and ~ (RFC 3986)\n",
    "valid_chars = list(string.ascii_letters)\n",
    "for x in range(0,10):\n",
    "    valid_chars.append(str(x))\n",
    "valid_chars.append('-')\n",
    "valid_chars.append('_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bridal-picking",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare1(str1, str2):\n",
    "    \"\"\"\n",
    "    Here we're going to amplify the lib strcmp delays to make the weakness easier to analyze:\n",
    "    1. evaluate strings l-to-r\n",
    "    2. dope a positive match with a tiny wait to emphasize the delay for demonstration\n",
    "    3. return a False match immediately\n",
    "    \"\"\"\n",
    "\n",
    "    one, two = list(str1), list(str2)\n",
    "    for x in range(0,len(one)):\n",
    "        if one[x] == two[x]:\n",
    "            time.sleep(.001)\n",
    "            pass\n",
    "        else:\n",
    "            return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "athletic-nightlife",
   "metadata": {},
   "source": [
    "### First example - guess the first letter\n",
    "\n",
    "Here we'll just run through each letter 1k times adding completion time to an array. Then we'll find the longest run of the letters.\n",
    "\n",
    "The original code here found the longest completion of a string and added it to the \"totals\" array, then we sorted the longest variant of 'totals'. This would be better if averaged every run, then added that to the dict. \n",
    "\n",
    "TODO:\n",
    "- add mean returns\n",
    "- add an evaluation loop that then uses that builds the string from the statistical stubs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "awful-rebate",
   "metadata": {},
   "outputs": [],
   "source": [
    "# in a long string we just want to guess the first character\n",
    "# mysecret = secrets.token_urlsafe()\n",
    "mysecret = 'rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUiQ'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "reduced-electric",
   "metadata": {},
   "outputs": [],
   "source": [
    "totals=[]\n",
    "for loop in range(1000):\n",
    "    stash = {}\n",
    "    for l in valid_chars:\n",
    "        mystr = l *10\n",
    "        start = attotime.attodatetime.now()\n",
    "        compare1(mystr, mysecret)\n",
    "        end = attotime.attodatetime.now()\n",
    "        duration = end - start\n",
    "        stash[mystr] = str(duration)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "labeled-advancement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('pppppppppp', '0:00:00.00020503997802735'),\n",
       " ('oooooooooo', '0:00:00.00020575523376465'),\n",
       " ('EEEEEEEEEE', '0:00:00.00020718574523926'),\n",
       " ('CCCCCCCCCC', '0:00:00.00020790100097656'),\n",
       " ('5555555555', '0:00:00.00020790100097657'),\n",
       " ('mmmmmmmmmm', '0:00:00.00020813941955566'),\n",
       " ('DDDDDDDDDD', '0:00:00.00020813941955566'),\n",
       " ('RRRRRRRRRR', '0:00:00.00020885467529297'),\n",
       " ('BBBBBBBBBB', '0:00:00.00020909309387207'),\n",
       " ('SSSSSSSSSS', '0:00:00.00020909309387207')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fastest\n",
    "sorted(stash.items(), key=lambda x: x[1])[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "casual-remark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('eeeeeeeeee', '0:00:00.00024604797363281'),\n",
       " ('XXXXXXXXXX', '0:00:00.00025177001953125'),\n",
       " ('MMMMMMMMMM', '0:00:00.00025510787963867'),\n",
       " ('6666666666', '0:00:00.00026392936706543'),\n",
       " ('----------', '0:00:00.00028324127197266'),\n",
       " ('FFFFFFFFFF', '0:00:00.00028610229492187'),\n",
       " ('GGGGGGGGGG', '0:00:00.00029516220092774'),\n",
       " ('WWWWWWWWWW', '0:00:00.00030899047851562'),\n",
       " ('LLLLLLLLLL', '0:00:00.00034999847412109'),\n",
       " ('rrrrrrrrrr', '0:00:00.00163507461547851')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slowest returns at the bottom\n",
    "# note how much slower lower case rrrrrrrrrr (leading character match) is compared to the rest\n",
    "sorted(stash.items(), key=lambda x: x[1])[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "personalized-picnic",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "extensive-rochester",
   "metadata": {},
   "source": [
    "### statistical analysis\n",
    "\n",
    "Currently incomplete.\n",
    "\n",
    "We'll run each letter comparison 10 times, then get the mean of those runs. After a loop choose the longest timed character as the next spot on the stub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "deluxe-police",
   "metadata": {},
   "outputs": [],
   "source": [
    "stash1 = {}\n",
    "for l in valid_chars:\n",
    "    totals=[]\n",
    "    for loop in range(10):\n",
    "        #mystr = l *10\n",
    "        start = attotime.attodatetime.now()\n",
    "        compare1(l, mysecret)\n",
    "        end = attotime.attodatetime.now()\n",
    "        ttime = end - start\n",
    "        duration = str(ttime).split(':')[2]\n",
    "        totals.append(float(duration))\n",
    "        #print(min(totals), max(totals), sum(totals) / len(totals))\n",
    "\n",
    "    stash1[l] = sum(totals) / len(totals)\n",
    "        \n",
    "# sorted(stash1.items(), key=lambda x: x[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "incorporate-motion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stash1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "intense-roman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('r', 0.0015602588653564479)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(stash1.items(), key=lambda x: x[1])[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faced-parade",
   "metadata": {},
   "source": [
    "### visualizing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "adjusted-yeast",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(stash.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "hybrid-german",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change the column names \n",
    "df.columns = ['string', 'duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "elegant-disney",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fixtimes(time):\n",
    "    \"\"\"\n",
    "    early version where duration was saved as a attotime string\n",
    "    \"\"\"\n",
    "    if isinstance(time, str):\n",
    "        return float(time.split(':')[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "objective-wallet",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change duration strings to be integers. only use the fractional nanoseconds in this case\n",
    "df['duration'] = df['duration'].apply(fixtimes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "appreciated-pavilion",
   "metadata": {},
   "source": [
    "**the plot**\n",
    "\n",
    "The plot is mildly borked. I want a list of xticks that show the string at 'x' where the Duration of 'y' is highest, but set_xticklabels doesn't correlate to y vals. Will fix this later. \n",
    "\n",
    "That said - you'll see that the high point duration matches the 'rrrrrrrrrr' entry from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "promising-validation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'duration')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(figsize=(18,6))\n",
    "df[df['duration']> .000222]['duration'].plot(figsize=(18,6))\n",
    "# not mapping strings to duration correctly\n",
    "plt.xticks(rotation=45)\n",
    "ax.set_xticklabels(df[df['duration']> .000222]['string'])\n",
    "#ax.set_xticklabels(df.string.to_list())\n",
    "# not working as I expected\n",
    "# ax.set_xlabel(df.string.to_list())\n",
    "ax.set_xlabel('string')\n",
    "ax.set_ylabel('duration')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "guilty-employment",
   "metadata": {},
   "source": [
    "**a histogram shows off the distributions**\n",
    "\n",
    "Most of the count returns immediately, but you can see on the right where the longest returns are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "reported-stick",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='duration', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a histogram shows off how obvious a good match is, compared to jitter\n",
    "sns.set(rc={'figure.figsize':(18,6)})\n",
    "plt.xticks(rotation=45)\n",
    "sns.distplot(df[df['duration']> .000222]['duration'],bins=150, rug=True, kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "senior-aging",
   "metadata": {},
   "source": [
    "## example of brute force strcmp analysis attacks \n",
    "\n",
    "Here's a dictionary matcher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "about-attack",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get a built-in dictionary\n",
    "wordsraw = open('/usr/share/dict/words','r').readlines()\n",
    "[x.strip('\\n') for x in wordsraw]\n",
    "words = [x.strip('\\n') for x in wordsraw]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "small-buying",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set our name as 'apple'\n",
    "name='apple'\n",
    "# a longer one\n",
    "name2 = 'unresourcefulness'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sorted-complexity",
   "metadata": {},
   "source": [
    "**This can take awhile to run**\n",
    "\n",
    "Just matching 'apple' from a dictionary of english words. Words farther down the alphabet will take longer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "informal-registration",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting at: 2021-09-17 18:28:24.229185\n",
      "Name is apple\n",
      "Finished. Total time: 0:00:23.12203621864318848\n"
     ]
    }
   ],
   "source": [
    "stash2 = {}\n",
    "print('starting at:', datetime.now())\n",
    "runstart = attotime.attodatetime.now()\n",
    "for w in range(0,len(words)):\n",
    "    start = attotime.attodatetime.now()\n",
    "    if compare1(words[w], name):\n",
    "            if compare1(words[w], name):\n",
    "                if words[w] == name:\n",
    "                    print('Name is', words[w])\n",
    "                    break\n",
    "            else:\n",
    "                end = attotime.attodatetime.now()\n",
    "                duration = end - start\n",
    "                stash2[words[w]] = str(duration)\n",
    "                print(words[w])\n",
    "                stub = s\n",
    "                break\n",
    "    else:\n",
    "        end = attotime.attodatetime.now()\n",
    "        duration = end - start\n",
    "        stash2[words[w]] = str(duration)\n",
    "        pass\n",
    "runend = attotime.attodatetime.now()\n",
    "print('Finished. Total time:', runend - runstart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "mediterranean-helmet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Angevin', '0:00:00.0002138614654541'),\n",
       " ('Amerindic', '0:00:00.00021457672119141'),\n",
       " ('Amerindian', '0:00:00.00021481513977051'),\n",
       " ('Aaronite', '0:00:00.00021505355834961'),\n",
       " ('Americanoid', '0:00:00.00021576881408691'),\n",
       " ('Appendiculata', '0:00:00.00021600723266602'),\n",
       " ('Anglicanum', '0:00:00.00021672248840332'),\n",
       " ('Aeacus', '0:00:00.00021696090698242'),\n",
       " ('Americaward', '0:00:00.00021696090698242'),\n",
       " ('Angetenar', '0:00:00.00021886825561523')]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(stash2.items(),key=lambda x: x[1])[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "empty-register",
   "metadata": {},
   "source": [
    "### Brute forcing letter by character\n",
    "\n",
    "using the timing analysis on local alphabet attacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "supposed-slave",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "un\n",
      "unr\n",
      "unre\n",
      "unres\n",
      "unreso\n",
      "unresou\n",
      "unresour\n",
      "unresourc\n",
      "unresource\n",
      "unresourcef\n",
      "unresourcefu\n",
      "unresourceful\n",
      "unresourcefuln\n",
      "unresourcefulne\n",
      "unresourcefulnes\n",
      "Name is unresourcefulness\n",
      "Name is unresourcefulness\n"
     ]
    }
   ],
   "source": [
    "\n",
    "stash3 = {}\n",
    "for c in valid_chars:\n",
    "    stub = c\n",
    "    for loop in range(0, len(name2)):\n",
    "        stublist = [stub + l for l in valid_chars]\n",
    "        for s in stublist:\n",
    "            start = attotime.attodatetime.now()\n",
    "            if compare1(s, name2):\n",
    "                if s == name2:\n",
    "                    print('Name is', s)\n",
    "                    break\n",
    "                else:\n",
    "                    end = attotime.attodatetime.now()\n",
    "                    duration = end - start\n",
    "                    stash3[s] = str(duration)\n",
    "                    print(s)\n",
    "                    stub = s\n",
    "                    break\n",
    "            else:\n",
    "                end = attotime.attodatetime.now()\n",
    "                duration = end - start\n",
    "                stash3[s] = str(duration)\n",
    "                pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cordless-stable",
   "metadata": {},
   "source": [
    "**The longest returns are at the bottom and correspond to getting closer to the answer**\n",
    "\n",
    "There is a little jumbling but it's clear that by only focusing on time it clusters better answers together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "sitting-therapy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('unresourcefulnesm', '0:00:00.01949715614318847'),\n",
       " ('unresourcefulnesa', '0:00:00.01952290534973144'),\n",
       " ('unresourcefulneb', '0:00:00.01956582069396973'),\n",
       " ('unresourcefulnesk', '0:00:00.0196380615234375'),\n",
       " ('unresourcefulnesh', '0:00:00.01964306831359864'),\n",
       " ('unresourcefulnesl', '0:00:00.01965808868408203'),\n",
       " ('unresourcefulnec', '0:00:00.01977396011352539'),\n",
       " ('unresourcefulnesn', '0:00:00.019989013671875'),\n",
       " ('unresourcefulnesf', '0:00:00.02000999450683593'),\n",
       " ('unresourcefulnesj', '0:00:00.02003812789916992'),\n",
       " ('unresourcefulnesi', '0:00:00.02007293701171875'),\n",
       " ('unresourcefulnesq', '0:00:00.02025604248046875'),\n",
       " ('unresourcefulnesg', '0:00:00.02032208442687989'),\n",
       " ('unresourcefulnese', '0:00:00.02042388916015625'),\n",
       " ('unresourcefulnee', '0:00:00.02042913436889649'),\n",
       " ('unresourcefulnesr', '0:00:00.02044820785522461'),\n",
       " ('unresourcefulnesb', '0:00:00.02046108245849609'),\n",
       " ('unresourcefulnes', '0:00:00.0205690860748291'),\n",
       " ('unresourcefulnesp', '0:00:00.02066922187805176'),\n",
       " ('unresourcefulnesc', '0:00:00.02082085609436035')]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The longest returns are at the bottom and correspond to getting closer to the answer\n",
    "sorted(stash3.items(), key=lambda x: x[1])[-20:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "falling-knowing",
   "metadata": {},
   "source": [
    "**and a modified dictionary word**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "broad-theater",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "or\n",
      "ora\n",
      "oran\n",
      "orang\n",
      "orang0\n",
      "orang00\n",
      "orang00t\n",
      "orang00t4\n",
      "orang00t4_\n",
      "Name is orang00t4_n\n",
      "Name is orang00t4_n\n"
     ]
    }
   ],
   "source": [
    "name='orang00t4_n'\n",
    "stash4 = {}\n",
    "for c in valid_chars:\n",
    "    stub = c\n",
    "    for loop in range(0, len(name)):\n",
    "        stublist = [stub + l for l in valid_chars]\n",
    "        for s in stublist:\n",
    "            start = attotime.attodatetime.now()\n",
    "            if compare1(s, name):\n",
    "                if s == name:\n",
    "                    print('Name is', s)\n",
    "                    break\n",
    "                else:\n",
    "                    end = attotime.attodatetime.now()\n",
    "                    duration = end - start\n",
    "                    stash4[s] = str(duration)\n",
    "                    print(s)\n",
    "                    stub = s\n",
    "                    break\n",
    "            else:\n",
    "                end = attotime.attodatetime.now()\n",
    "                duration = end - start\n",
    "                stash4[s] = str(duration)\n",
    "                pass\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "manual-spanish",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('orang00t4_f', '0:00:00.01232790946960449'),\n",
       " ('orang00t4A', '0:00:00.01233291625976563'),\n",
       " ('orang00t4Q', '0:00:00.01233506202697754'),\n",
       " ('orang00t4M', '0:00:00.01236200332641601'),\n",
       " ('orang00t4L', '0:00:00.01236224174499512'),\n",
       " ('orang00t4_j', '0:00:00.01240086555480957'),\n",
       " ('orang00t4J', '0:00:00.01240921020507812'),\n",
       " ('orang00t4_c', '0:00:00.01241874694824219'),\n",
       " ('orang00t4_g', '0:00:00.01261687278747558'),\n",
       " ('orang00t4_d', '0:00:00.01262688636779785'),\n",
       " ('orang00t4N', '0:00:00.01263189315795899'),\n",
       " ('orang00t46', '0:00:00.01274228096008301'),\n",
       " ('orang00t4_e', '0:00:00.01292610168457031'),\n",
       " ('orang00t4_b', '0:00:00.0131230354309082'),\n",
       " ('orang00t4_a', '0:00:00.01313185691833496'),\n",
       " ('orang00t4_i', '0:00:00.01323080062866211'),\n",
       " ('orang00t4_k', '0:00:00.01343059539794922'),\n",
       " ('orang00t4_l', '0:00:00.01357483863830566'),\n",
       " ('orang00t4_m', '0:00:00.01358890533447265'),\n",
       " ('orang00t4_', '0:00:00.01370596885681152')]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(stash4.items(), key=lambda x: x[1])[-20:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fancy-container",
   "metadata": {},
   "source": [
    "## Testing the time difference with our long string\n",
    "\n",
    "\n",
    "This will take slightly longer for a 41 character string, but trivial on a modern laptop.\n",
    "\n",
    "\n",
    "mysecret = 'rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUiQ'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "primary-passport",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting at: 2021-09-17 18:29:51.843824\n",
      "rM\n",
      "rMA\n",
      "rMAw\n",
      "rMAwz\n",
      "rMAwzI\n",
      "rMAwzI_\n",
      "rMAwzI_5\n",
      "rMAwzI_52\n",
      "rMAwzI_52t\n",
      "rMAwzI_52tV\n",
      "rMAwzI_52tVS\n",
      "rMAwzI_52tVSv\n",
      "rMAwzI_52tVSvg\n",
      "rMAwzI_52tVSvgK\n",
      "rMAwzI_52tVSvgKz\n",
      "rMAwzI_52tVSvgKzR\n",
      "rMAwzI_52tVSvgKzRo\n",
      "rMAwzI_52tVSvgKzRo3\n",
      "rMAwzI_52tVSvgKzRo3P\n",
      "rMAwzI_52tVSvgKzRo3PJ\n",
      "rMAwzI_52tVSvgKzRo3PJ0\n",
      "rMAwzI_52tVSvgKzRo3PJ0G\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_D\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Da\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Dan\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7j\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jl\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jla\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6v\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vT\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTk\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkO\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOU\n",
      "rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUi\n",
      "Name is rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUiQ\n",
      "Name is rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUiQ\n",
      "Finished. Total time: 0:02:11.95618581771850586\n"
     ]
    }
   ],
   "source": [
    "print('starting at:', datetime.now())\n",
    "runstart = attotime.attodatetime.now()\n",
    "stash5 = {}\n",
    "for c in valid_chars:\n",
    "    stub = c\n",
    "    for loop in range(0, len(mysecret)):\n",
    "        stublist = [stub + l for l in valid_chars]\n",
    "        for s in stublist:\n",
    "            start = attotime.attodatetime.now()\n",
    "            if compare1(s, mysecret):\n",
    "                if s == mysecret:\n",
    "                    print('Name is', s)\n",
    "                    break\n",
    "                else:\n",
    "                    end = attotime.attodatetime.now()\n",
    "                    duration = end - start\n",
    "                    stash5[s] = str(duration)\n",
    "                    print(s)\n",
    "                    stub = s\n",
    "                    break\n",
    "            else:\n",
    "                end = attotime.attodatetime.now()\n",
    "                duration = end - start\n",
    "                stash5[s] = str(duration)\n",
    "                pass\n",
    "runend = attotime.attodatetime.now()\n",
    "print('Finished. Total time:', runend - runstart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "opening-picking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('wj', '0:00:00.00020766258239746'),\n",
       " ('W1', '0:00:00.00020790100097656'),\n",
       " ('wd', '0:00:00.00020885467529297'),\n",
       " ('wW', '0:00:00.00020885467529297'),\n",
       " ('w2', '0:00:00.00020885467529297')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(stash5.items(), key=lambda x: x[1])[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "opening-number",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUiI', '0:00:00.05340313911437988'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUic', '0:00:00.05345988273620606'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUil', '0:00:00.05347490310668946'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUij', '0:00:00.05352807044982911'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUc', '0:00:00.05353784561157226'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUin', '0:00:00.05362987518310547'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUit', '0:00:00.05378103256225586'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUib', '0:00:00.05379605293273926'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUip', '0:00:00.05381107330322265'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUir', '0:00:00.05389690399169922'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUiM', '0:00:00.05401873588562012'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUiw', '0:00:00.05427503585815429'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUiG', '0:00:00.05428004264831543'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUg', '0:00:00.05505990982055665'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUio', '0:00:00.05537605285644531'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danb', '0:00:00.05628490447998047'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUi', '0:00:00.05629491806030273'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUb', '0:00:00.0577230453491211'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkH', '0:00:00.05944228172302246'),\n",
       " ('rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkG', '0:00:00.07227611541748046')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(stash5.items(), key=lambda x: x[1])[-20:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yellow-module",
   "metadata": {},
   "source": [
    "### How would this compare to hash comparison?\n",
    "\n",
    "This will start will the dictionary word 'apple'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "extra-photographer",
   "metadata": {},
   "outputs": [],
   "source": [
    "def comparehash(token, secret):\n",
    "    \"\"\"\n",
    "    we're going to use the old md5 hash to simplify the demonstration\n",
    "    \"\"\"\n",
    "    htoken, hsecret = hashlib.md5(token.encode('utf-8')), hashlib.md5(secret.encode('utf-8'))\n",
    "    one, two = list(htoken.hexdigest()), list(hsecret.hexdigest())\n",
    "    for x in range(0,len(one)):\n",
    "        if one[x] == two[x]:\n",
    "            time.sleep(.001)\n",
    "            pass\n",
    "        else:\n",
    "            return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "level-tobago",
   "metadata": {},
   "outputs": [],
   "source": [
    "totals=[]\n",
    "for loop in range(1000):\n",
    "    stash10 = {}\n",
    "    for l in valid_chars:\n",
    "        mystr = l *10\n",
    "        start = attotime.attodatetime.now()\n",
    "        comparehash(mystr, mysecret)\n",
    "        end = attotime.attodatetime.now()\n",
    "        duration = end - start\n",
    "        stash10[mystr] = str(duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "labeled-cement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('vvvvvvvvvv', '0:00:00.00034904479980469'),\n",
       " ('VVVVVVVVVV', '0:00:00.00035595893859863'),\n",
       " ('FFFFFFFFFF', '0:00:00.00036096572875976'),\n",
       " ('8888888888', '0:00:00.00036215782165528'),\n",
       " ('YYYYYYYYYY', '0:00:00.00037193298339844'),\n",
       " ('rrrrrrrrrr', '0:00:00.00045371055603027'),\n",
       " ('9999999999', '0:00:00.00048375129699707'),\n",
       " ('tttttttttt', '0:00:00.00051307678222656'),\n",
       " ('uuuuuuuuuu', '0:00:00.00054621696472168'),\n",
       " ('hhhhhhhhhh', '0:00:00.00150609016418457')]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(stash10.items(), key=lambda x: x[1])[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "coated-wrapping",
   "metadata": {},
   "outputs": [],
   "source": [
    "df10 = pd.DataFrame(stash10.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "illegal-expansion",
   "metadata": {},
   "outputs": [],
   "source": [
    "df10.columns = ['string', 'duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "surgical-electricity",
   "metadata": {},
   "outputs": [],
   "source": [
    "df10['duration'] = df10['duration'].apply(fixtimes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "removed-waste",
   "metadata": {},
   "source": [
    "**It's easy to see how much flatter the response times are**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "going-monaco",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'duration')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df10.plot(figsize=(18,6))\n",
    "df10[df10['duration']> .000222]['duration'].plot(figsize=(18,6))\n",
    "# not mapping strings to duration correctly\n",
    "plt.xticks(rotation=45)\n",
    "ax.set_xticklabels(df[df['duration']> .000222]['string'])\n",
    "#ax.set_xticklabels(df.string.to_list())\n",
    "# not working as I expected\n",
    "# ax.set_xlabel(df.string.to_list())\n",
    "ax.set_xlabel('string')\n",
    "ax.set_ylabel('duration')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "illegal-courage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='duration', ylabel='Density'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(18,6)})\n",
    "plt.xticks(rotation=45)\n",
    "sns.distplot(df10['duration'],bins=150, rug=True, kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "higher-volleyball",
   "metadata": {},
   "source": [
    "## How does this compare to a constant time return?\n",
    "\n",
    "Now lets add a buffer of time to the outputs. I don't "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "secondary-factor",
   "metadata": {},
   "outputs": [],
   "source": [
    "def comparehashconstant(token, secret):\n",
    "    \"\"\"\n",
    "    we're going to use the old md5 hash to simplify the demonstration\n",
    "    and add a wait before returning either a match or a failure.\n",
    "    Buffering in a dumb way, but this should make it harder to determine the validity of a guess.\n",
    "    \"\"\"\n",
    "    htoken, hsecret = hashlib.md5(token.encode('utf-8')), hashlib.md5(secret.encode('utf-8'))\n",
    "    one, two = list(htoken.hexdigest()), list(hsecret.hexdigest())\n",
    "    for x in range(0,len(one)):\n",
    "        if one[x] == two[x]:\n",
    "            time.sleep(.001)\n",
    "            pass\n",
    "        else:\n",
    "            time.sleep(.02)\n",
    "            return False\n",
    "    time.sleep(.01)\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "marked-quantity",
   "metadata": {},
   "outputs": [],
   "source": [
    "totals=[]\n",
    "for loop in range(1000):\n",
    "    stash11 = {}\n",
    "    for l in valid_chars:\n",
    "        mystr = l *10\n",
    "        start = attotime.attodatetime.now()\n",
    "        comparehashconstant(mystr, mysecret)\n",
    "        end = attotime.attodatetime.now()\n",
    "        duration = end - start\n",
    "        stash11[mystr] = str(duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "radical-sugar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('2222222222', '0:00:00.0242149829864502'),\n",
       " ('OOOOOOOOOO', '0:00:00.0242161750793457'),\n",
       " ('VVVVVVVVVV', '0:00:00.02432489395141602'),\n",
       " ('KKKKKKKKKK', '0:00:00.0244133472442627'),\n",
       " ('XXXXXXXXXX', '0:00:00.0247349739074707'),\n",
       " ('pppppppppp', '0:00:00.02481603622436524'),\n",
       " ('FFFFFFFFFF', '0:00:00.02485299110412598'),\n",
       " ('__________', '0:00:00.02506589889526367'),\n",
       " ('nnnnnnnnnn', '0:00:00.02530097961425781'),\n",
       " ('xxxxxxxxxx', '0:00:00.02539587020874024')]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(stash11.items(), key=lambda x: x[1])[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "covered-sculpture",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>string</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>rrrrrrrrrr</td>\n",
       "      <td>0.000454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        string  duration\n",
       "17  rrrrrrrrrr  0.000454"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10[df10['string'] == 'rrrrrrrrrr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "labeled-certification",
   "metadata": {},
   "outputs": [],
   "source": [
    "df11 = pd.DataFrame(stash11.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "declared-solution",
   "metadata": {},
   "outputs": [],
   "source": [
    "df11.columns = ['string', 'duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "behind-archive",
   "metadata": {},
   "outputs": [],
   "source": [
    "df11['duration'] = df11['duration'].apply(fixtimes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comprehensive-period",
   "metadata": {},
   "source": [
    "**The plots are all over the place, but none of the top 10 chars are 'r'. First letter guessing is obfuscated.**\n",
    "\n",
    "first letter 'r' is the 17th hit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "improving-vocabulary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'rMAwzI_52tVSvgKzRo3PJ0G7_Danw7jlaZ-6vTkOUiQ'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mysecret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "acting-factory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>string</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>xxxxxxxxxx</td>\n",
       "      <td>0.025396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>nnnnnnnnnn</td>\n",
       "      <td>0.025301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>__________</td>\n",
       "      <td>0.025066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>FFFFFFFFFF</td>\n",
       "      <td>0.024853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>pppppppppp</td>\n",
       "      <td>0.024816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>XXXXXXXXXX</td>\n",
       "      <td>0.024735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>KKKKKKKKKK</td>\n",
       "      <td>0.024413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>VVVVVVVVVV</td>\n",
       "      <td>0.024325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>OOOOOOOOOO</td>\n",
       "      <td>0.024216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2222222222</td>\n",
       "      <td>0.024215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        string  duration\n",
       "23  xxxxxxxxxx  0.025396\n",
       "13  nnnnnnnnnn  0.025301\n",
       "63  __________  0.025066\n",
       "31  FFFFFFFFFF  0.024853\n",
       "15  pppppppppp  0.024816\n",
       "49  XXXXXXXXXX  0.024735\n",
       "36  KKKKKKKKKK  0.024413\n",
       "47  VVVVVVVVVV  0.024325\n",
       "40  OOOOOOOOOO  0.024216\n",
       "54  2222222222  0.024215"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11.sort_values(by='duration', ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "crude-census",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>string</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>rrrrrrrrrr</td>\n",
       "      <td>0.024012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        string  duration\n",
       "17  rrrrrrrrrr  0.024012"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11[df11['string'] == 'rrrrrrrrrr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "remarkable-stand",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'duration')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df11.plot(figsize=(18,6))\n",
    "df11['duration'].plot(figsize=(18,6))\n",
    "# not mapping strings to duration correctly\n",
    "plt.xticks(rotation=45)\n",
    "ax.set_xticklabels(df11['string'])\n",
    "#ax.set_xticklabels(df.string.to_list())\n",
    "# not working as I expected\n",
    "# ax.set_xlabel(df.string.to_list())\n",
    "ax.set_xlabel('string')\n",
    "ax.set_ylabel('duration')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "small-assessment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='duration', ylabel='Density'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(18,6)})\n",
    "plt.xticks(rotation=45)\n",
    "sns.distplot(df11['duration'],bins=150, rug=True, kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6f3d76b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}