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analyzing-malware-family-relationships-with-malpedia

analyzing-malware-family-relationships-with-malpedia

github.com/mukul975安全35.2k349 次安装8 周前更新
代码仓库

关于 analyzing-malware-family-relationships-with-malpedia

面向安全分析人员的技能,可查询 Malpedia 并梳理恶意软件家族、别名、威胁组织及检测规则,支持威胁情报分析与检测验证。

主要功能

  1. 01查询 Malpedia 家族数据
  2. 02梳理家族与组织关系
  3. 03提取并编译 YARA 规则
  4. 04记录加载器与载荷链

适用场景

  1. 01调查安全事件中的恶意软件
  2. 02构建基于 YARA 的检测
  3. 03梳理威胁组织的恶意软件生态

安装

复制下面的命令,在终端中运行。

$npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-malware-family-relationships-with-malpedia

安装后在 Claude Code 中输入/analyzing-malware-family-relationships-with-malpedia确认已加载。

包含的文件

除 SKILL.md 外,安装时一起下载的参考资料和脚本。

LICENSE
references/
api-reference.md
scripts/
agent.py

详细介绍

使用 Malpedia 分析恶意软件家族关系

概述

Malpedia 是由 Fraunhofer FKIE 维护的协作平台,收录了恶意软件家族及其别名、YARA 规则、威胁行为者关联和参考报告。它记录了 2,600 多个恶意软件家族,是理解恶意软件谱系、跟踪变种演变以及将恶意软件与特定威胁组织关联起来的权威资源。本技能涵盖查询 Malpedia API、映射恶意软件家族关系、提取用于检测的 YARA 规则,以及构建对手所用恶意软件生态的情报。

何时使用

  • 在调查需要分析恶意软件家族与 Malpedia 关系的安全事件时
  • 在为该领域构建检测规则或威胁狩猎查询时
  • 当 SOC 分析师需要针对此类分析的结构化流程时
  • 在验证相关攻击技术的安全监控覆盖范围时

前提条件

  • Python 3.9+,并安装 requests、yara-python、stix2 库
  • Malpedia API 密钥(在 https://malpedia.caad.fkie.fraunhofer.de/ 注册)
  • 了解恶意软件分类和命名惯例
  • 熟悉用于检测的 YARA 规则语法
  • 可访问恶意软件样本以进行验证(可选)

关键概念

Malpedia 数据模型

Malpedia 将恶意软件组织为家族(例如 "win.cobalt_strike"),每个家族包含:别名(厂商特定名称,如 "Beacon"、"CobaltStrike")、YARA 规则(社区和厂商贡献)、行为者关联(使用该家族的威胁组织)、参考报告(记录该家族的 CTI 报告)以及样本哈希(每个变种的代表性样本)。

恶意软件家族命名

Malpedia 使用 platform.family_name 格式(例如 win.emotet、elf.mirai、apk.flubot)。平台包括 win(Windows)、elf(Linux)、apk(Android)、osx(macOS)和 py(Python)。这种标准化命名解决了“名称繁多”的问题,即不同厂商为同一恶意软件分配不同名称。

家族关系

恶意软件家族之间存在多种关系,包括:父子关系(代码重用、分支)、加载器-载荷关系(Emotet 加载 TrickBot,TrickBot 加载 Ryuk)、共同作者关系(同一威胁行为者开发多个工具)以及基础设施共享(共用 C2 框架)。

工作流

步骤 1:查询 Malpedia API 获取恶意软件家族
import requests
import json
from collections import defaultdict

class MalpediaClient:
    BASE_URL = "https://malpedia.caad.fkie.fraunhofer.de/api"

    def __init__(self, api_key):
        self.headers = {"Authorization": f"apitoken {api_key}"}

    def get_family_list(self):
        """Get list of all malware families."""
        resp = requests.get(f"{self.BASE_URL}/list/families",
                           headers=self.headers, timeout=30)
        if resp.status_code == 200:
            families = resp.json()
            print(f"[+] Malpedia: {len(families)} malware families")
            return families
        return {}

    def get_family_info(self, family_name):
        """Get detailed information about a malware family."""
        resp = requests.get(f"{self.BASE_URL}/get/family/{family_name}",
                           headers=self.headers, timeout=30)
        if resp.status_code == 200:
            info = resp.json()
            print(f"[+] Family: {family_name}")
            print(f"    Aliases: {info.get('alt_names', [])}")
            print(f"    Actors: {[a.get('value', '') for a in info.get('attribution', [])]}")
            print(f"    URLs: {len(info.get('urls', []))} references")
            return info
        print(f"[-] Family not found: {family_name}")
        return None

    def get_family_yara(self, family_name):
        """Get YARA rules for a malware family."""
        resp = requests.get(f"{self.BASE_URL}/get/yara/{family_name}",
                           headers=self.headers, timeout=30)
        if resp.status_code == 200:
            rules = resp.json()
            rule_count = sum(len(v) for v in rules.values()) if isinstance(rules, dict) else 0
            print(f"[+] YARA rules for {family_name}: {rule_count} rules")
            return rules
        return {}

    def get_actor_families(self, actor_name):
        """Get malware families associated with a threat actor."""
        resp = requests.get(f"{self.BASE_URL}/get/actor/{actor_name}",
                           headers=self.headers, timeout=30)
        if resp.status_code == 200:
            data = resp.json()
            families = data.get("families", {})
            print(f"[+] {actor_name}: {len(families)} malware families")
            return data
        return {}

    def search_families(self, keyword):
        """Search families by keyword."""
        all_families = self.get_family_list()
        matches = {
            name: info for name, info in all_families.items()
            if keyword.lower() in name.lower()
            or keyword.lower() in str(info.get("alt_names", [])).lower()
        }
        print(f"[+] Search '{keyword}': {len(matches)} matches")
        return matches

client = MalpediaClient("YOUR_MALPEDIA_API_KEY")
families = client.get_family_list()
emotet_info = client.get_family_info("win.emotet")
步骤 2:映射恶意软件家族关系
class MalwareFamilyMapper:
    def __init__(self, malpedia_client):
        self.client = malpedia_client
        self.relationship_graph = defaultdict(list)

    def map_actor_ecosystem(self, actor_name):
        """Map the malware ecosystem used by a threat actor."""
        actor_data = self.client.get_actor_families(actor_name)
        families = actor_data.get("families", {})

        ecosystem = {
            "actor": actor_name,
            "families": [],
            "family_count": len(families),
        }

        for family_name in families:
            info = self.client.get_family_info(family_name)
            if info:
                ecosystem["families"].append({
                    "name": family_name,
                    "aliases": info.get("alt_names", []),
                    "description": info.get("description", "")[:200],
                    "shared_actors": [
                        a.get("value", "")
                        for a in info.get("attribution", [])
                    ],
                    "reference_count": len(info.get("urls", [])),
                })

        print(f"\n=== {actor_name} Malware Ecosystem ===")
        for fam in ecosystem["families"]:
            shared = [a for a in fam["shared_actors"] if a != actor_name]
            print(f"  {fam['name']}")
            print(f"    Aliases: {fam['aliases'][:5]}")
            if shared:
                print(f"    Also used by: {shared}")

        return ecosystem

    def find_shared_tooling(self, actor_names):
        """Find malware families shared between threat actors."""
        actor_families = {}
        for actor in actor_names:
            data = self.client.get_actor_families(actor)
            actor_families[actor] = set(data.get("families", {}).keys())

        # Find overlaps
        shared = {}
        for i, actor1 in enumerate(actor_names):
            for actor2 in actor_names[i+1:]:
                common = actor_families[actor1] & actor_families[actor2]
                if common:
                    shared[f"{actor1} <-> {actor2}"] = sorted(common)

        print(f"\n=== Shared Tooling Analysis ===")
        for pair, families in shared.items():
            print(f"  {pair}: {len(families)} shared families")
            for f in families[:5]:
                print(f"    - {f}")

        return shared

    def build_loader_payload_chain(self, family_name):
        """Build the loader-payload delivery chain for a family."""
        info = self.client.get_family_info(family_name)
        if not info:
            return {}

        chain = {
            "family": family_name,
            "description": info.get("description", ""),
            "known_loaders": [],
            "known_payloads": [],
        }

        # Common known delivery chains
        known_chains = {
            "win.emotet": {"loaders": ["email/macro"], "payloads": ["win.trickbot", "win.qakbot", "win.cobalt_strike"]},
            "win.trickbot": {"loaders": ["win.emotet"], "payloads": ["win.ryuk", "win.conti", "win.cobalt_strike"]},
            "win.qakbot": {"loaders": ["email/macro", "win.emotet"], "payloads": ["win.cobalt_strike", "win.blackbasta"]},
            "win.cobalt_strike": {"loaders": ["win.emotet", "win.trickbot", "win.qakbot"], "payloads": ["ransomware"]},
        }

        if family_name in known_chains:
            chain["known_loaders"] = known_chains[family_name]["loaders"]
            chain["known_payloads"] = known_chains[family_name]["payloads"]

        return chain

mapper = MalwareFamilyMapper(client)
ecosystem = mapper.map_actor_ecosystem("Wizard Spider")
shared = mapper.find_shared_tooling(["Wizard Spider", "FIN7", "Lazarus Group"])
chain = mapper.build_loader_payload_chain("win.emotet")
步骤 3:提取并编译 YARA 规则
def compile_yara_ruleset(client, family_names, output_file="malware_yara_rules.yar"):
    """Compile YARA rules for multiple malware families."""
    all_rules = []
    for family in family_names:
        yara_data = client.get_family_yara(family)
        if isinstance(yara_data, dict):
            for source, rules in yara_data.items():
                if isinstance(rules, list):
                    for rule in rules:
                        all_rules.append(f"// Source: {source} - Family: {family}\n{rule}")
                elif isinstance(rules, str):
                    all_rules.append(f"// Source: {source} - Family: {family}\n{rules}")

    with open(output_file, "w") as f:
        f.write(f"// Malpedia YARA Rules - {len(all_rules)} rules\n")
        f.write(f"// Families: {', '.join(family_names)}\n\n")
        for rule in all_rules:
            f.write(rule + "\n\n")

    print(f"[+] Compiled {len(all_rules)} YARA rules to {output_file}")
    return all_rules

compile_yara_ruleset(client, ["win.emotet", "win.trickbot", "win.cobalt_strike"])

验证标准

  • 成功查询 Malpedia API 获取恶意软件家族
  • 检索到包含别名、行为者和参考的家族信息
  • 正确映射行为者-家族关系
  • 识别出行为者之间共享的工具
  • 提取并编译 YARA 规则用于检测
  • 记录加载器-载荷链以用于威胁情报

参考资料

评论

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