HELK/docker/helk-jupyter/notebooks/sigma/win_encoded_frombase64string.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoded FromBase64String\n",
    "Detects a base64 encoded FromBase64String keyword in a process command line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rule Content\n",
    "```\n",
    "- title: Encoded FromBase64String\n",
    "  id: fdb62a13-9a81-4e5c-a38f-ea93a16f6d7c\n",
    "  status: experimental\n",
    "  description: Detects a base64 encoded FromBase64String keyword in a process command\n",
    "    line\n",
    "  author: Florian Roth\n",
    "  date: 2019/08/24\n",
    "  tags:\n",
    "  - attack.t1086\n",
    "  - attack.t1140\n",
    "  - attack.execution\n",
    "  - attack.defense_evasion\n",
    "  logsource:\n",
    "    category: process_creation\n",
    "    product: windows\n",
    "    service: null\n",
    "  detection:\n",
    "    selection:\n",
    "      CommandLine|base64offset|contains: ::FromBase64String\n",
    "    condition: selection\n",
    "  fields:\n",
    "  - CommandLine\n",
    "  - ParentCommandLine\n",
    "  falsepositives:\n",
    "  - unknown\n",
    "  level: critical\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying Elasticsearch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from elasticsearch import Elasticsearch\n",
    "from elasticsearch_dsl import Search\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize Elasticsearch client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "es = Elasticsearch(['http://helk-elasticsearch:9200'])\n",
    "searchContext = Search(using=es, index='logs-*', doc_type='doc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Elasticsearch Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = searchContext.query('query_string', query='process_command_line.keyword:(*OjpGcm9tQmFzZTY0U3RyaW5n* OR *o6RnJvbUJhc2U2NFN0cmluZ* OR *6OkZyb21CYXNlNjRTdHJpbm*)')\n",
    "response = s.execute()\n",
    "if response.success():\n",
    "    df = pd.DataFrame((d.to_dict() for d in s.scan()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 4
}