mirror of https://github.com/infosecn1nja/HELK.git
121 lines
2.6 KiB
Plaintext
121 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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"# Suspicious Log Entries\n",
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"Detects suspicious log entries in Linux log files"
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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: Suspicious Log Entries\n",
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" id: f64b6e9a-5d9d-48a5-8289-e1dd2b3876e1\n",
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" description: Detects suspicious log entries in Linux log files\n",
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" author: Florian Roth\n",
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" logsource:\n",
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" product: linux\n",
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" service: null\n",
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" category: null\n",
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" detection:\n",
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" keywords:\n",
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" - entered promiscuous mode\n",
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" - Deactivating service\n",
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" - Oversized packet received from\n",
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" - imuxsock begins to drop messages\n",
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" condition: keywords\n",
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" falsepositives:\n",
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" - Unknown\n",
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" level: medium\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='\\*.keyword:(*entered\\ promiscuous\\ mode* OR *Deactivating\\ service* OR *Oversized\\ packet\\ received\\ from* OR *imuxsock\\ begins\\ to\\ drop\\ messages*)')\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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