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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"# PowerShell Base64 Encoded Shellcode\n",
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"Detects Base64 encoded Shellcode"
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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: PowerShell Base64 Encoded Shellcode\n",
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" id: 2d117e49-e626-4c7c-bd1f-c3c0147774c8\n",
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" description: Detects Base64 encoded Shellcode\n",
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" status: experimental\n",
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" references:\n",
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" - https://twitter.com/cyb3rops/status/1063072865992523776\n",
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" author: Florian Roth\n",
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" date: 2018/11/17\n",
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" tags:\n",
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" - attack.defense_evasion\n",
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" - attack.t1036\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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" selection1:\n",
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" CommandLine: '*AAAAYInlM*'\n",
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" selection2:\n",
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" CommandLine:\n",
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" - '*OiCAAAAYInlM*'\n",
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" - '*OiJAAAAYInlM*'\n",
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" condition: selection1 and selection2\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:*AAAAYInlM* AND process_command_line.keyword:(*OiCAAAAYInlM* OR *OiJAAAAYInlM*))')\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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