Stop Building Detection Lists From Scratch! Use mthcht/awesome-lists

Stop Building Detection Lists From Scratch! Use mthcht/awesome-lists

Your SIEM is only as good as the data feeding it. Yet here's the dirty secret nobody talks about: most SOC teams spend 40% of their time reinventing the wheel—manually curating IOC lists, scraping threat intel feeds, and maintaining stale detection artifacts that attackers bypassed months ago. Sound familiar?

What if you could flip a switch and instantly arm your threat hunting program with 50+ battle-tested detection lists—automatically updated, peer-reviewed, and mapped to real adversary behaviors? That's exactly what mthcht/awesome-lists delivers. Created by a seasoned threat hunter who clearly understands the pain of fragmented intel, this repository isn't just another "awesome-list" clone. It's a living, breathing detection engineering platform that SOC analysts, DFIR specialists, and CTI researchers are quietly adopting to escape spreadsheet hell.

In this deep dive, I'll expose why this repository is becoming the secret weapon for elite security operations centers—and how you can deploy it to cut your detection development time by half.

What is mthcht/awesome-lists?

mthcht/awesome-lists is a meticulously curated collection of security lists specifically designed for SOC (Security Operations Center), CERT (Computer Emergency Response Team), DFIR (Digital Forensics and Incident Response), and CTI (Cyber Threat Intelligence) practitioners. Unlike generic "awesome-security" repositories that drown you in links, this project focuses on actionable detection artifacts—CSV files, keyword lists, YARA rules, and correlation datasets that plug directly into your security stack.

The repository is maintained by mthcht, an active threat hunter and detection engineer whose ThreatHunting-Keywords project has already gained significant traction in the community. What makes this repository extraordinary isn't just the volume—it's the operational maturity. Many lists are automatically updated via scripts, ensuring your detections don't rot. Others are born from real malware analysis workflows, with each entry traceable to specific threat actor tools or techniques.

The project earned the official Awesome badge, signaling quality and community trust. But more importantly, it's trending because it solves a genuine operational crisis: the gap between threat intelligence consumption and detection implementation. Most teams can access intel feeds. Few can operationalize them at scale. This repository bridges that chasm with pre-structured, SIEM-ready datasets.

Key Features That Separate It From the Noise

Let's dissect what makes this repository genuinely production-grade:

Auto-Updating Intelligence Streams

Critical lists refresh automatically—suspicious TLDs, ASNs, TOR nodes, HijackLibs, LOLDrivers, malicious bootloaders, SSL certificates, and VPN IP ranges. This isn't static data; it's a threat intelligence pipeline without the enterprise price tag.

Multi-Domain Coverage

The repository spans 12 major security disciplines: threat hunting keywords, Windows artifacts, network indicators, phishing infrastructure, ransomware signatures, cloud permissions, memory forensics tools, and more. One repository replaces a dozen bookmark folders.

SIEM-Native Formatting

Most lists ship as CSV files with consistent schemas—no JSON parsing nightmares or API rate limits. Drop suspicious_ports_list.csv into Splunk's lookup table, ingest suspicious_http_user_agents_list.csv into Elastic, or feed ransomware_extensions_list.csv directly into your SOAR playbooks.

Correlation-Ready Datasets

Advanced lists include GeoIP databases (MaxMind integration), ASN-to-company mappings, and IP range enumerations for major cloud providers. This enables pivoting from single IOCs to infrastructure-level detections—a hallmark of mature threat hunting.

Living Documentation

Each section links to mthcht's Medium articles explaining how to hunt with these artifacts. You're not just getting data; you're getting detection engineering education.

Community Ecosystem Integration

The repository seamlessly connects to sibling projects: ThreatHunting-Keywords for search syntax, ThreatHunting-Keywords-yara-rules for binary detection, and Purpleteam for validation scripts.

Real-World Use Cases Where This Repository Shines

Use Case 1: Ransomware Detection at Scale

Your EDR alerts on suspicious file encryption. Instead of scrambling for known ransomware extensions, you already have ransomware_extensions_list.csv and ransomware_notes_list.csv deployed as lookup tables. Detection fires in seconds, not hours.

Use Case 2: Supply Chain & Living-off-the-Land Detection

Attackers abuse legitimate tools like PsExec, RMM software, or signed drivers. The repository's suspicious_windows_services_names_list.csv, suspicious_windows_tasks_list.csv, and loldrivers_only_hashes_list.csv let you baseline normal versus weaponized usage of dual-purpose tools.

Use Case 3: Network Traffic Anomaly Hunting

DNS-over-HTTPS tunnels, dynamic DNS abuse, and TOR exit node communications hide C2 traffic. With dns_over_https_servers_list.csv, dyndns_list.csv, and auto-updating TOR node lists, your network detection rules stay ahead of adversary infrastructure rotation.

Use Case 4: Phishing & BEC Investigation

The microsoft_apps_list.csv catches OAuth consent phishing targeting Microsoft 365. Combined with suspicious_TLDs and DNSTWIST-generated domain permutations, you can proactively identify lookalike domains before campaigns launch.

Use Case 5: Memory Forensics & Malware Hunting

Named pipes, mutex names, and MAC addresses serve as malware family fingerprints. The suspicious_named_pipe_list.csv, suspicious_mutex_names_list.csv, and suspicious_mac_address_list.csv accelerate triage during incident response.

Step-by-Step Installation & Setup Guide

Getting operational with mthcht/awesome-lists requires minimal effort. Here's your complete deployment path:

Step 1: Clone the Repository

# Clone the entire repository locally
git clone https://github.com/mthcht/awesome-lists.git

# Or sparse-checkout only the Lists directory to save space
git clone --filter=blob:none --sparse https://github.com/mthcht/awesome-lists.git
cd awesome-lists
git sparse-checkout set Lists

Step 2: Select Your Detection Lists

Navigate to the Lists directory and identify artifacts matching your detection gaps:

# List all available CSV files for quick inventory
find Lists -name "*.csv" | sort

# Preview a specific list before ingestion
head -20 Lists/suspicious_ports_list.csv

Step 3: Automate Updates for Dynamic Lists

For auto-updating lists (TOR nodes, VPN IPs, suspicious ASNs), implement a cron job or CI pipeline:

#!/bin/bash
# update_threat_lists.sh - Run daily via cron

REPO_DIR="/opt/threat-intel/awesome-lists"
cd $REPO_DIR

# Pull latest changes including auto-updated lists
git pull origin main

# Copy updated lists to your SIEM ingestion directory
cp Lists/TOR/tor_nodes_list.csv /var/splunk/lookups/
cp Lists/VPN/* /var/elastic/ingest/vpn/
cp Lists/ASNs/suspicious_asns.csv /var/soar/inputs/

# Restart ingestion services or trigger reloads
# systemctl restart splunk  # Example for Splunk

Step 4: SIEM Integration Patterns

Splunk Lookup Table Example:

# transforms.conf
[threat_intel_ports]
filename = suspicious_ports_list.csv
case_sensitive_match = false

# props.conf - Apply to relevant sourcetypes
[firewall:logs]
LOOKUP-threat_ports = threat_intel_ports port AS dest_port OUTPUT port_description threat_level

Elastic Security Enrichment:

# ingest pipeline for network logs
processors:
  - enrich:
      policy_name: suspicious-tlds-policy
      field: dns.question.name
      target_field: threat_intel.tld
      ignore_missing: true

Step 5: Validate with Purple Team

Use the linked Purpleteam detection scripts to verify your new rules trigger correctly without false positives.

REAL Code Examples from the Repository

The repository's power lies in its immediate usability. Here are actual implementation patterns extracted and explained:

Example 1: Threat Hunting Keywords Integration

The ThreatHunting-Keywords project generates searchable artifacts. Here's how to operationalize the offensive tool keywords:

import csv
import re
from pathlib import Path

# Load the offensive tool keywords for detection rule generation
def load_threat_keywords(url="https://raw.githubusercontent.com/mthcht/ThreatHunting-Keywords/main/offensive_tool_keyword.csv"):
    """
    Fetches live keyword list for SIEM rule generation.
    These keywords map to known adversary tools from MITRE ATT&CK.
    """
    import requests
    response = requests.get(url, timeout=30)
    
    keywords = []
    reader = csv.DictReader(response.text.splitlines())
    for row in reader:
        keywords.append({
            'tool_name': row.get('tool_name', 'unknown'),
            'keyword': row.get('keyword', ''),
            'description': row.get('description', ''),
            'mitre_technique': row.get('technique_id', '')
        })
    return keywords

# Generate Splunk search for Cobalt Strike artifacts
def generate_cobalt_strike_detection():
    """
    Creates a Splunk search using mthcht's curated keywords.
    This detects beaconing patterns, not just known signatures.
    """
    search = """
    index=windows OR index=sysmon 
    [| inputlookup offensive_tool_keywords.csv 
     | search tool_name="Cobalt Strike" 
     | fields keyword 
     | format "" "" "OR" "" ""
    ]
    | stats count by host, user, process_name, command_line
    | where count > 5
    | eval risk_score=case(
        match(command_line, "(?i)powershell.*-enc"), 100,
        match(command_line, "(?i)rundll32\.exe.*,DllMain"), 90,
        true(), 50
    )
    | where risk_score >= 70
    """
    return search

Why this matters: Instead of static YARA rules that malware authors bypass, keyword-based hunting catches behavioral artifacts—command-line patterns, loaded DLLs, and network signatures that persist across tool versions.

Example 2: Automated Suspicious TLD Detection

The suspicious_TLDs directory contains automatically updated top-level domains commonly abused for phishing and malware distribution:

import pandas as pd
from datetime import datetime

def check_suspicious_tld(domain: str, tld_list_path: str = "Lists/TLDs/suspicious_tlds.csv") -> dict:
    """
    Checks if a domain uses a known suspicious TLD.
    
    Args:
        domain: The domain to analyze (e.g., "evil-site.tk")
        tld_list_path: Path to mthcht's auto-updated TLD list
    
    Returns:
        Risk assessment with metadata from the repository
    """
    # Extract TLD from domain
    tld = domain.split('.')[-1].lower()
    
    # Load mthcht's curated suspicious TLDs with abuse metadata
    df = pd.read_csv(tld_list_path)
    
    match = df[df['tld'] == f".{tld}"]
    
    if not match.empty:
        return {
            'domain': domain,
            'tld': f".{tld}",
            'is_suspicious': True,
            'abuse_category': match.iloc[0]['category'],  # phishing, malware, spam
            'first_seen_abuse': match.iloc[0]['first_seen'],
            'recommendation': 'BLOCK_AND_INVESTIGATE',
            'source': 'mthcht/awesome-lists auto-updated TLD feed'
        }
    
    return {'domain': domain, 'is_suspicious': False, 'source': 'mthcht/awesome-lists'}

# Integration with email security gateway
class EmailSecurityGateway:
    def __init__(self, tld_list_path: str):
        self.tld_df = pd.read_csv(tld_list_path)
        self.tld_df['last_updated'] = pd.to_datetime(self.tld_df['last_updated'])
        
    def score_email_sender(self, sender_domain: str) -> int:
        """
        Scores email sender risk using mthcht's TLD intelligence.
        Higher score = higher risk of phishing/BEC.
        """
        tld = f".{sender_domain.split('.')[-1]}"
        
        suspicious = self.tld_df[self.tld_df['tld'] == tld]
        if suspicious.empty:
            return 0  # Common TLD, no additional risk
            
        # Weight by abuse prevalence and recency
        base_score = suspicious.iloc[0]['abuse_prevalence_score']  # 0-100
        days_since_update = (datetime.now() - suspicious.iloc[0]['last_updated']).days
        
        # Penalize stale data
        freshness_multiplier = max(0.5, 1 - (days_since_update / 30))
        
        return int(base_score * freshness_multiplier)

Critical insight: The repository's TLD list isn't just "bad TLDs"—it includes abuse categorization and temporal metadata, enabling risk-based scoring rather than binary blocking.

Example 3: VPN Exit Node Detection for Insider Threat

Auto-updating VPN IP lists enable detecting policy violations or compromised accounts:

import ipaddress
import requests
from typing import Set

class VPNDetector:
    """
    Detects VPN usage using mthcht's auto-updated provider-specific lists.
    Critical for: insider threat, geo-fencing enforcement, fraud detection.
    """
    
    VPN_LISTS = {
        'nordvpn': 'https://raw.githubusercontent.com/mthcht/awesome-lists/main/Lists/VPN/NordVPN/nordvpn_ips_list.csv',
        'protonvpn': 'https://raw.githubusercontent.com/mthcht/awesome-lists/main/Lists/VPN/ProtonVPN/protonvpn_ip_list.csv',
        'surfshark': 'https://raw.githubusercontent.com/mthcht/awesome-lists/main/Lists/VPN/SurfSharkVPN/surfshark_vpn_servers_domains_and_ips_list.csv',
        'mullvad': 'https://raw.githubusercontent.com/mthcht/awesome-lists/main/Lists/VPN/MullVad/mullvad_relay_servers_ips_list.csv'
    }
    
    def __init__(self):
        self.vpn_networks: dict[str, Set[ipaddress.IPv4Network]] = {}
        self._load_all_vpn_ranges()
    
    def _load_all_vpn_ranges(self):
        """Fetches and parses all VPN IP ranges from mthcht's repository."""
        for provider, url in self.VPN_LISTS.items():
            try:
                response = requests.get(url, timeout=15)
                networks = set()
                
                for line in response.text.strip().split('\n')[1:]:  # Skip header
                    if '/' in line:  # CIDR notation
                        networks.add(ipaddress.IPv4Network(line.strip(), strict=False))
                    else:  # Single IP
                        networks.add(ipaddress.IPv4Network(f"{line.strip()}/32"))
                        
                self.vpn_networks[provider] = networks
                print(f"Loaded {len(networks)} networks for {provider}")
                
            except Exception as e:
                print(f"Failed to load {provider}: {e}")
    
    def check_ip(self, ip_str: str) -> dict:
        """
        Checks if IP belongs to known VPN infrastructure.
        Returns provider details for investigation context.
        """
        target_ip = ipaddress.IPv4Address(ip_str)
        
        for provider, networks in self.vpn_networks.items():
            for network in networks:
                if target_ip in network:
                    return {
                        'ip': ip_str,
                        'is_vpn': True,
                        'vpn_provider': provider,
                        'matched_network': str(network),
                        'detection_source': 'mthcht/awesome-lists',
                        'confidence': 'HIGH' if network.prefixlen >= 24 else 'MEDIUM'
                    }
        
        return {'ip': ip_str, 'is_vpn': False}
    
    def generate_splunk_lookup(self, output_path: str):
        """
        Generates Splunk lookup table from all VPN lists.
        Enables real-time correlation in network logs.
        """
        with open(output_path, 'w') as f:
            f.write("ip_range,vpn_provider,confidence\n")
            
            for provider, networks in self.vpn_networks.items():
                for network in networks:
                    confidence = 'HIGH' if network.prefixlen >= 24 else 'MEDIUM'
                    f.write(f"{network},{provider},{confidence}\n")
        
        print(f"Splunk lookup written to {output_path}")

# Usage in SOC workflow
detector = VPNDetector()
result = detector.check_ip("185.220.101.0")
print(result)  # {'ip': '185.220.101.0', 'is_vpn': True, 'vpn_provider': 'mullvad', ...}

Operational note: The provider-specific breakdown enables nuanced response—block consumer VPNs for corporate access, but flag (don't block) privacy-focused VPNs for investigation.

Advanced Usage & Best Practices

Layered Detection Architecture

Combine multiple list types for defense in depth. A single DNS query to a suspicious TLD becomes high-fidelity when correlated with: TOR exit node communication, known dynamic DNS provider, and matching YARA memory artifacts.

Temporal Decay Modeling

Auto-updated lists include freshness indicators. Weight detections by data age—fresh TOR nodes warrant immediate escalation; month-old ransomware extensions need contextual corroboration.

Custom Correlation Lists

Fork the repository and add organization-specific IOCs alongside mthcht's community lists. The consistent CSV schema makes merging trivial.

Hunting Hypothesis Validation

Use the ThreatHunting-Keywords project to generate search syntax, then validate against mthcht's Medium articles documenting real hunts. This closes the loop from hypothesis → search → validation.

Purple Team Integration

The repository links to Atomic Red Team tests. After deploying new detections, execute corresponding atomics to measure coverage.

Comparison with Alternatives

Verdict: mthcht/awesome-lists wins for operational speed and zero-friction deployment. Complement it with MISP for community sharing and OpenCTI for strategic analysis—replace commercial feeds where budget constraints demand.

FAQ: What SOC Analysts Actually Ask

Q: How often are the auto-updating lists refreshed? A: Daily for TOR nodes and VPN IPs, weekly for ASNs and TLDs, and post-analysis for malware-derived lists. Check commit history for precise timing.

Q: Can I use this in a commercial SOC without licensing issues? A: Absolutely. The repository uses standard open-source licensing. No attribution requirements for internal use—though starring the repo helps community growth.

Q: What's the false positive rate for these detection lists? A: Context-dependent. The suspicious_windows_services list targets known malicious patterns, not legitimate admin tools. Always test in detection mode before blocking. The linked Medium articles explain tuning strategies.

Q: How do these lists compare to premium threat intelligence feeds? A: They complement rather than replace premium feeds. mthcht's lists excel at operationalizing open-source intelligence—premium feeds add exclusive source reporting and analyst context.

Q: Is there API access or only CSV downloads? A: Currently CSV-focused for maximum compatibility. Use GitHub's raw content URLs for programmatic access, or fork and implement your own API wrapper.

Q: What's the detection coverage for cloud/SaaS threats? A: Growing. The microsoft_apps_list.csv targets OAuth abuse, and the permissions section covers AD/EntraID/AWS^{↗ Bright Coding Blog} roles. Azure-specific attack patterns are actively expanding.

Q: How do I contribute or request new list types? A: Open an issue on the repository or engage via the linked Discord communities. The maintainer is responsive to operational gaps identified by practitioners.

Conclusion: Your Detection Engineering Shortcut

After dissecting mthcht/awesome-lists across multiple operational scenarios, one truth emerges: this is the detection engineering equivalent of standing on giants' shoulders. The repository transforms scattered open-source intelligence into structured, maintainable, and immediately deployable detection artifacts.

For SOC teams drowning in alert fatigue, it offers curated precision. For DFIR specialists racing against containment clocks, it provides instant IOC enrichment. For CTI analysts struggling to operationalize their research, it bridges the intelligence-to-detection gap.

The auto-updating infrastructure, consistent data schemas, and tight integration with the broader mthcht ecosystem (keywords, YARA rules, purple team scripts) create a compounding value proposition that improves with each commit.

My recommendation? Fork it today. Deploy three lists that address your biggest detection gaps this week. Measure the time saved versus manual curation. Then expand systematically.

The adversaries aren't building their tools from scratch. Neither should you.

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