mirror of https://github.com/infosecn1nja/HELK.git
129 lines
2.8 KiB
Plaintext
129 lines
2.8 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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"# Encoded FromBase64String\n",
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"Detects a base64 encoded FromBase64String keyword in a process command line"
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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: Encoded FromBase64String\n",
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" id: fdb62a13-9a81-4e5c-a38f-ea93a16f6d7c\n",
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" status: experimental\n",
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" description: Detects a base64 encoded FromBase64String keyword in a process command\n",
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" line\n",
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" author: Florian Roth\n",
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" date: 2019/08/24\n",
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" tags:\n",
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" - attack.t1086\n",
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" - attack.t1140\n",
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" - attack.execution\n",
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" - attack.defense_evasion\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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" CommandLine|base64offset|contains: ::FromBase64String\n",
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" condition: selection\n",
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" fields:\n",
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" - CommandLine\n",
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" - ParentCommandLine\n",
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" falsepositives:\n",
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" - unknown\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_command_line.keyword:(*OjpGcm9tQmFzZTY0U3RyaW5n* OR *o6RnJvbUJhc2U2NFN0cmluZ* OR *6OkZyb21CYXNlNjRTdHJpbm*)')\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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