{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Webshell Remote Command Execution\n", "Detects posible command execution by web application/web shell" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule Content\n", "```\n", "- title: Webshell Remote Command Execution\n", " id: c0d3734d-330f-4a03-aae2-65dacc6a8222\n", " status: experimental\n", " description: Detects posible command execution by web application/web shell\n", " tags:\n", " - attack.persistence\n", " - attack.t1100\n", " references:\n", " - personal experience\n", " author: Ilyas Ochkov, Beyu Denis, oscd.community\n", " date: 2019/10/12\n", " modified: 2019/11/04\n", " logsource:\n", " product: linux\n", " service: auditd\n", " category: null\n", " detection:\n", " selection:\n", " type: SYSCALL\n", " SYSCALL: execve\n", " key: detect_execve_www\n", " condition: selection\n", " falsepositives:\n", " - Admin activity\n", " - Crazy web applications\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='(type:\"SYSCALL\" AND SYSCALL:\"execve\" AND key:\"detect_execve_www\")')\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 }