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

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Suspicious Commandline Escape\n",
"Detects suspicious process that use escape characters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content\n",
"```\n",
"- title: Suspicious Commandline Escape\n",
" id: f0cdd048-82dc-4f7a-8a7a-b87a52b6d0fd\n",
" description: Detects suspicious process that use escape characters\n",
" status: experimental\n",
" references:\n",
" - https://twitter.com/vysecurity/status/885545634958385153\n",
" - https://twitter.com/Hexacorn/status/885553465417756673\n",
" - https://twitter.com/Hexacorn/status/885570278637678592\n",
" - https://www.fireeye.com/blog/threat-research/2017/06/obfuscation-in-the-wild.html\n",
" - http://www.windowsinspired.com/understanding-the-command-line-string-and-arguments-received-by-a-windows-program/\n",
" author: juju4\n",
" modified: 2018/12/11\n",
" tags:\n",
" - attack.defense_evasion\n",
" - attack.t1140\n",
" logsource:\n",
" category: process_creation\n",
" product: windows\n",
" service: null\n",
" detection:\n",
" selection:\n",
" CommandLine:\n",
" - ^h^t^t^p\n",
" - h\"t\"t\"p\n",
" condition: selection\n",
" falsepositives:\n",
" - False positives depend on scripts and administrative tools used in the monitored\n",
" environment\n",
" level: low\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:(\"\\^h\\^t\\^t\\^p\" OR \"h\\\"t\\\"t\\\"p\")')\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
}