HELK/docker/helk-jupyter/notebooks/sigma/lnx_auditd_user_discovery.i...

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# System Owner or User Discovery\n",
"Adversaries may use the information from System Owner/User Discovery during automated discovery to shape follow-on behaviors, including whether or not the adversary fully infects the target and/or attempts specific actions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content\n",
"```\n",
"- title: System Owner or User Discovery\n",
" id: 9a0d8ca0-2385-4020-b6c6-cb6153ca56f3\n",
" status: experimental\n",
" description: Adversaries may use the information from System Owner/User Discovery\n",
" during automated discovery to shape follow-on behaviors, including whether or\n",
" not the adversary fully infects the target and/or attempts specific actions.\n",
" author: Timur Zinniatullin, oscd.community\n",
" date: 2019/10/21\n",
" references:\n",
" - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1033/T1033.yaml\n",
" logsource:\n",
" product: linux\n",
" service: auditd\n",
" category: null\n",
" detection:\n",
" selection:\n",
" type: EXECVE\n",
" a0:\n",
" - users\n",
" - w\n",
" - who\n",
" condition: selection\n",
" falsepositives:\n",
" - Admin activity\n",
" level: low\n",
" tags:\n",
" - attack.discovery\n",
" - attack.t1033\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='(type:\"EXECVE\" AND a0:(\"users\" OR \"w\" OR \"who\"))')\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
}