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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"# SSHD Error Message CVE-2018-15473\n",
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"Detects exploitation attempt using public exploit code for CVE-2018-15473"
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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: SSHD Error Message CVE-2018-15473\n",
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" id: 4c9d903d-4939-4094-ade0-3cb748f4d7da\n",
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" description: Detects exploitation attempt using public exploit code for CVE-2018-15473\n",
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" references:\n",
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" - https://github.com/Rhynorater/CVE-2018-15473-Exploit\n",
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" author: Florian Roth\n",
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" date: 2017/08/24\n",
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" logsource:\n",
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" product: linux\n",
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" service: sshd\n",
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" category: null\n",
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" detection:\n",
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" keywords:\n",
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" - 'error: buffer_get_ret: trying to get more bytes 1907 than in buffer 308 [preauth]'\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='error\\:\\ buffer_get_ret\\:\\ trying\\ to\\ get\\ more\\ bytes\\ 1907\\ than\\ in\\ buffer\\ 308\\ \\[preauth\\]')\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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