mirror of https://github.com/infosecn1nja/HELK.git
127 lines
2.7 KiB
Plaintext
127 lines
2.7 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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"# Security Eventlog Cleared\n",
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"Some threat groups tend to delete the local 'Security' Eventlog using certain utitlities"
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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: Security Eventlog Cleared\n",
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" id: f2f01843-e7b8-4f95-a35a-d23584476423\n",
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" description: Some threat groups tend to delete the local 'Security' Eventlog using\n",
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" certain utitlities\n",
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" tags:\n",
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" - attack.defense_evasion\n",
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" - attack.t1070\n",
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" - car.2016-04-002\n",
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" author: Florian Roth\n",
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" logsource:\n",
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" product: windows\n",
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" service: security\n",
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" category: null\n",
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" detection:\n",
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" selection:\n",
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" EventID:\n",
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" - 517\n",
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" - 1102\n",
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" condition: selection\n",
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" falsepositives:\n",
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" - Rollout of log collection agents (the setup routine often includes a reset of\n",
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" the local Eventlog)\n",
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" - System provisioning (system reset before the golden image creation)\n",
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" level: high\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-endpoint-winevent-security-*', 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='event_id:(\"517\" OR \"1102\")')\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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