{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Encoded FromBase64String\n", "Detects a base64 encoded FromBase64String keyword in a process command line" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule Content\n", "```\n", "- title: Encoded FromBase64String\n", " id: fdb62a13-9a81-4e5c-a38f-ea93a16f6d7c\n", " status: experimental\n", " description: Detects a base64 encoded FromBase64String keyword in a process command\n", " line\n", " author: Florian Roth\n", " date: 2019/08/24\n", " tags:\n", " - attack.t1086\n", " - attack.t1140\n", " - attack.execution\n", " - attack.defense_evasion\n", " logsource:\n", " category: process_creation\n", " product: windows\n", " service: null\n", " detection:\n", " selection:\n", " CommandLine|base64offset|contains: ::FromBase64String\n", " condition: selection\n", " fields:\n", " - CommandLine\n", " - ParentCommandLine\n", " falsepositives:\n", " - unknown\n", " level: critical\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Querying Elasticsearch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import Libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from elasticsearch import Elasticsearch\n", "from elasticsearch_dsl import Search\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialize Elasticsearch client" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "es = Elasticsearch(['http://helk-elasticsearch:9200'])\n", "searchContext = Search(using=es, index='logs-*', doc_type='doc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run Elasticsearch Query" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = searchContext.query('query_string', query='process_command_line.keyword:(*OjpGcm9tQmFzZTY0U3RyaW5n* OR *o6RnJvbUJhc2U2NFN0cmluZ* OR *6OkZyb21CYXNlNjRTdHJpbm*)')\n", "response = s.execute()\n", "if response.success():\n", " df = pd.DataFrame((d.to_dict() for d in s.scan()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show Results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.head()" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 4 }