mirror of https://github.com/infosecn1nja/HELK.git
124 lines
2.6 KiB
Plaintext
124 lines
2.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# CrackMapExecWin\n",
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"Detects CrackMapExecWin Activity as Described by NCSC"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Rule Content\n",
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"```\n",
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"- title: CrackMapExecWin\n",
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" id: 04d9079e-3905-4b70-ad37-6bdf11304965\n",
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" description: Detects CrackMapExecWin Activity as Described by NCSC\n",
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" status: experimental\n",
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" references:\n",
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" - https://www.ncsc.gov.uk/alerts/hostile-state-actors-compromising-uk-organisations-focus-engineering-and-industrial-control\n",
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" tags:\n",
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" - attack.g0035\n",
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" author: Markus Neis\n",
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" logsource:\n",
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" category: process_creation\n",
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" product: windows\n",
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" service: null\n",
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" detection:\n",
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" selection:\n",
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" Image:\n",
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" - '*\\crackmapexec.exe'\n",
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" condition: selection\n",
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" falsepositives:\n",
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" - None\n",
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" level: critical\n",
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"\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Querying Elasticsearch"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Import Libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from elasticsearch import Elasticsearch\n",
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"from elasticsearch_dsl import Search\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Initialize Elasticsearch client"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"es = Elasticsearch(['http://helk-elasticsearch:9200'])\n",
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"searchContext = Search(using=es, index='logs-*', doc_type='doc')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Run Elasticsearch Query"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"s = searchContext.query('query_string', query='process_path.keyword:(*\\\\crackmapexec.exe)')\n",
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"response = s.execute()\n",
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"if response.success():\n",
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" df = pd.DataFrame((d.to_dict() for d in s.scan()))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Show Results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df.head()"
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]
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}
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],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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