HELK/docker/helk-jupyter/notebooks/sigma/sysmon_wmi_event_subscripti...

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# WMI Event Subscription\n",
"Detects creation of WMI event subscription persistence method"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content\n",
"```\n",
"- title: WMI Event Subscription\n",
" id: 0f06a3a5-6a09-413f-8743-e6cf35561297\n",
" status: experimental\n",
" description: Detects creation of WMI event subscription persistence method\n",
" references:\n",
" - https://attack.mitre.org/techniques/T1084/\n",
" tags:\n",
" - attack.t1084\n",
" - attack.persistence\n",
" author: Tom Ueltschi (@c_APT_ure)\n",
" date: 2019/01/12\n",
" logsource:\n",
" product: windows\n",
" service: sysmon\n",
" category: null\n",
" detection:\n",
" selector:\n",
" EventID:\n",
" - 19\n",
" - 20\n",
" - 21\n",
" condition: selector\n",
" falsepositives:\n",
" - exclude legitimate (vetted) use of WMI event subscription in your network\n",
" level: high\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-endpoint-winevent-sysmon-*', 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='event_id:(\"19\" OR \"20\" OR \"21\")')\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
}