mirror of https://github.com/infosecn1nja/HELK.git
124 lines
2.6 KiB
Plaintext
124 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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"# Python SQL Exceptions\n",
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"Generic rule for SQL exceptions in Python according to PEP 249"
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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: Python SQL Exceptions\n",
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" id: 19aefed0-ffd4-47dc-a7fc-f8b1425e84f9\n",
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" description: Generic rule for SQL exceptions in Python according to PEP 249\n",
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" author: Thomas Patzke\n",
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" references:\n",
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" - https://www.python.org/dev/peps/pep-0249/#exceptions\n",
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" logsource:\n",
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" category: application\n",
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" product: python\n",
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" service: null\n",
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" detection:\n",
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" exceptions:\n",
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" - DataError\n",
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" - IntegrityError\n",
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" - ProgrammingError\n",
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" - OperationalError\n",
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" condition: exceptions\n",
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" falsepositives:\n",
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" - Application bugs\n",
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" - Penetration testing\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:(*DataError* OR *IntegrityError* OR *ProgrammingError* OR *OperationalError*)')\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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