DIY Guide: Building a Custom DNS Blacklist with an Editor

Automating Threat Prevention with a DNS Blacklist Editor### Introduction

Automating threat prevention at the DNS layer is an efficient way to reduce exposure to malware, phishing, command-and-control (C2) servers, and other web-based threats before they reach users and endpoints. A DNS blacklist editor — a tool that allows administrators to create, manage, and apply lists of blocked domains — is central to this strategy. When combined with automation, these editors can ingest threat intelligence, apply policies, and respond to incidents in near real time, significantly reducing the window of exposure.

Why DNS-layer blocking matters

DNS is a choke point for internet-bound traffic: most malicious campaigns rely on domain lookups to host payloads, manage C2, or redirect users to phishing sites. Blocking bad domains at the DNS level has several advantages:

• Prevention before connection: Stops lookups for malicious domains before a TCP connection or HTTP request is made.
• Low resource cost: DNS filtering requires minimal compute compared with deep packet inspection or endpoint detection.
• Network-wide coverage: A DNS blacklist applies to any device using the filtered resolver, including unmanaged or IoT devices.
• Simplicity and speed: Updates to a blacklist take effect immediately across clients relying on the resolver.

Core components of a DNS Blacklist Editor

A robust DNS blacklist editor supports these essential functions:

• Ingestion: importing threat intelligence feeds, lists, or manual entries.
• Normalization: validating and standardizing domains, removing wildcards or malformed entries.
• Policy management: defining block/allow actions, time-based rules, and exceptions.
• Versioning and audit logs: tracking changes and who made them.
• Distribution: pushing updates to DNS resolvers, DNS firewalls, or recursive servers.
• Automation hooks: APIs, webhooks, or integrations with SIEM, SOAR, or threat intel platforms.

Building an automated workflow

Here’s a typical automated workflow using a DNS blacklist editor:

1. Threat feed ingestion: scheduled pulls from feeds (public, commercial, or internal) and receiving alerts from a monitoring platform.
2. Triage and enrichment: automated enrichment (WHOIS, passive DNS, reputation scores) to reduce false positives.
3. Scoring and rules: compute risk scores and apply threshold rules for automatic blocking versus human review.
4. Staging: add new entries to a staging list for review; allow rapid rollback.
5. Deployment: publish approved lists to production resolvers or DNS firewalls via API.
6. Monitoring and feedback: log blocked queries and feed telemetry back to the enrichment pipeline for tuning.

Example automation tools to integrate: cron jobs or task schedulers, Python scripts, SIEM (Splunk, Elastic), SOAR (Demisto, Swimlane), and orchestration via CI/CD pipelines.

Practical considerations and best practices

• Use multiple intelligence sources to reduce single-feed bias and false positives.
• Apply risk-based thresholds: block only high-confidence domains automatically; route medium-confidence entries to review.
• Maintain allowlists for essential services and internal domains to avoid outages.
• Implement staged rollouts and quick rollback mechanisms to mitigate accidental mass-blocking.
• Keep change logs and require approvals for high-impact actions.
• Monitor DNS query logs for spikes in blocked lookups — they can indicate active compromise or a misconfiguration.
• Protect the blacklist editor itself with RBAC, MFA, and secure API keys.
• Test blacklist updates in a sandbox or on a subset of resolvers before wide deployment.

Example: Automating feeds into a blacklist editor (conceptual)

A simple automation example—no code here—would be:

• A scheduled job downloads multiple threat feeds in CSV/TXT format.
• A parsing script normalizes domain names, removes duplicates, and enriches entries with WHOIS and passive DNS.
• A scoring function assigns confidence levels; high-confidence domains are pushed to the editor via its API to the “block” list. Medium-confidence domains are sent to an analyst queue.
• The editor version is incremented and deployed to resolvers; telemetry is monitored for anomalies.

Common pitfalls and how to avoid them

• Over-blocking: avoid overly broad wildcard rules that can block legitimate subdomains. Use precise patterns and exceptions.
• Stale entries: rotate and expire entries to prevent permanent blocking of domains that become safe.
• Performance: very large lists can slow down some resolvers; use efficient formats (e.g., binary trees, hashed lookups) and tiered deployment.
• Dependency on third-party feeds: validate feed quality and maintain internal sources.

Measuring effectiveness

Track these metrics to evaluate automation impact:

• Blocked queries per day and per category (malware, phishing, etc.).
• Reduction in endpoint detections correlated with DNS blocks.
• Time from feed receipt to block deployment.
• False positive rate (user-reported or support tickets).
• Incidents prevented or shortened due to early DNS blocking.

Advanced topics

• DNS over HTTPS/TLS (DoH/DoT): encrypted DNS can bypass local resolvers. Mitigate by controlling network-level access or using enterprise DoH solutions.
• Dynamic allow/block based on device posture: integrate with NAC and endpoint telemetry for conditional policies.
• Machine learning: use anomaly detection on DNS query patterns to surface suspicious domains not present in feeds.
• Federation: share anonymized telemetry and curated blocks across organizational units while preserving privacy.

Conclusion

Automating threat prevention with a DNS blacklist editor reduces exposure to web-based threats and improves response time. The key is combining reliable feed ingestion, strong enrichment and scoring, staged deployment, and continuous monitoring. Done well, DNS-layer automation acts like an early-warning gatekeeper — stopping many threats before they ever reach users or endpoints.