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

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Suspicious Use of Procdump\n",
"Detects suspicious uses of the SysInternals Procdump utility by using a special command line parameter in combination with the lsass.exe process. This way we're also able to catch cases in which the attacker has renamed the procdump executable."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content\n",
"```\n",
"- title: Suspicious Use of Procdump\n",
" id: 5afee48e-67dd-4e03-a783-f74259dcf998\n",
" description: Detects suspicious uses of the SysInternals Procdump utility by using\n",
" a special command line parameter in combination with the lsass.exe process. This\n",
" way we're also able to catch cases in which the attacker has renamed the procdump\n",
" executable.\n",
" status: experimental\n",
" references:\n",
" - Internal Research\n",
" author: Florian Roth\n",
" date: 2018/10/30\n",
" modified: 2019/10/14\n",
" tags:\n",
" - attack.defense_evasion\n",
" - attack.t1036\n",
" - attack.credential_access\n",
" - attack.t1003\n",
" - car.2013-05-009\n",
" logsource:\n",
" category: process_creation\n",
" product: windows\n",
" service: null\n",
" detection:\n",
" selection1:\n",
" CommandLine:\n",
" - '* -ma *'\n",
" selection2:\n",
" CommandLine:\n",
" - '* lsass*'\n",
" selection3:\n",
" CommandLine:\n",
" - '* -ma ls*'\n",
" condition: ( selection1 and selection2 ) or selection3\n",
" falsepositives:\n",
" - Unlikely, because no one should dump an lsass process memory\n",
" - Another tool that uses the command line switches of Procdump\n",
" level: medium\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:(*\\ \\-ma\\ *) AND process_command_line.keyword:(*\\ lsass*)) OR process_command_line.keyword:(*\\ \\-ma\\ ls*))')\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
}