{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Webshell Detection by Keyword\n", "Detects webshells that use GET requests by keyword searches in URL strings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule Content\n", "```\n", "- title: Webshell Detection by Keyword\n", " id: 7ff9db12-1b94-4a79-ba68-a2402c5d6729\n", " description: Detects webshells that use GET requests by keyword searches in URL\n", " strings\n", " author: Florian Roth\n", " logsource:\n", " category: webserver\n", " product: null\n", " service: null\n", " detection:\n", " keywords:\n", " - =whoami\n", " - =net%20user\n", " - =cmd%20/c%20\n", " condition: keywords\n", " fields:\n", " - client_ip\n", " - vhost\n", " - url\n", " - response\n", " falsepositives:\n", " - Web sites like wikis with articles on os commands and pages that include the os\n", " commands in the URLs\n", " - User searches in search boxes of the respective website\n", " level: high\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='\\*.keyword:(*\\=whoami* OR *\\=net%20user* OR *\\=cmd%20\\/c%20*)')\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 }