{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Crypto Miner User Agent\n", "Detects suspicious user agent strings used by crypto miners in proxy logs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule Content\n", "```\n", "- title: Crypto Miner User Agent\n", " id: fa935401-513b-467b-81f4-f9e77aa0dd78\n", " status: experimental\n", " description: Detects suspicious user agent strings used by crypto miners in proxy\n", " logs\n", " references:\n", " - https://github.com/xmrig/xmrig/blob/da22b3e6c45825f3ac1f208255126cb8585cd4fc/src/base/kernel/Platform_win.cpp#L65\n", " - https://github.com/xmrig/xmrig/blob/427b6516e0550200c17ca28675118f0fffcc323f/src/version.h\n", " author: Florian Roth\n", " date: 2019/10/21\n", " logsource:\n", " category: proxy\n", " product: null\n", " service: null\n", " detection:\n", " selection:\n", " c-useragent:\n", " - XMRig *\n", " - ccminer*\n", " condition: selection\n", " fields:\n", " - ClientIP\n", " - c-uri\n", " - c-useragent\n", " falsepositives:\n", " - Unknown\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='c-useragent.keyword:(XMRig\\ * OR ccminer*)')\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 }