{"id":7701,"date":"2025-06-02T12:00:00","date_gmt":"2025-06-02T17:00:00","guid":{"rendered":"https:\/\/www.threatstop.com\/blog\/enhancing-dns-security-with-machine-learning"},"modified":"2025-06-02T12:00:00","modified_gmt":"2025-06-02T17:00:00","slug":"enhancing-dns-security-with-machine-learning","status":"publish","type":"post","link":"https:\/\/ddi.mohflo.net\/index.php\/2025\/06\/02\/enhancing-dns-security-with-machine-learning\/","title":{"rendered":"Enhancing DNS Security with Machine Learning"},"content":{"rendered":"
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We\u2019re proud to announce that ThreatSTOP is now using Machine Learning (ML)<\/strong><\/span> to enhance the protections we deliver to our customers.<\/p>\n

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This isn\u2019t a marketing gimmick or a checkbox feature. We\u2019re not throwing around terms like \u201cAI\u201d for attention. We\u2019re applying real machine learning models to solve a real-world cybersecurity problem: detecting and stopping malicious domains that traditional methods miss.  When we visited RSA2025 this year, “AI” was baked into many company names and solutions on the show floor.  This announcement isn’t marketing, it’s something we’re doing.<\/p>\n

Our models are trained on massive volumes of DNS telemetry and threat intelligence. They\u2019re designed to identify subtle patterns and domain characteristics that may indicate phishing, command and control, or other malicious activity. This intelligence is then used to augment specific targets<\/strong><\/span> within the ThreatSTOP ecosystem\u2014improving the accuracy and responsiveness of our protective policies.<\/p>\n

The best part?<\/strong><\/span> Our customers don\u2019t need to take any action to benefit. As these enhancements roll out, protections will automatically improve behind the scenes, with no configuration changes required on your end.<\/p>\n

We\u2019re beginning this rollout in an experimental phase. If you\u2019re interested in participating and seeing how ML-driven protections can further improve your security posture, reach out to us at support@threatstop.com<\/strong><\/span><\/a> and let us know.<\/span><\/p>\n

Our Protective DNS solutions, DNS Defense Cloud and DNS Defense, are starting to use ML-driven analysis to stop malicious domains before they can impact your organization. Whether it\u2019s phishing domains that mimic real brands or infrastructure built using random-looking domain names, our system is designed to catch threats early and automatically.<\/p>\n

The Problem with Lookalike Domains<\/strong><\/h4>\n

Cyber attackers are constantly registering domains that look just like familiar websites. A small typo, an extra character, or a clever substitution can be enough to trick someone into clicking a dangerous link. These domains are often missed by traditional filters.<\/p>\n

ThreatSTOP\u2019s new ML engines help spot and block these threats before they ever reach your users.<\/p>\n

1. Identifying Similar Domains<\/strong><\/h4>\n

Our system uses a proximity scoring algorithm to measure how closely a new domain resembles a known good one. For example, if someone creates \u201cmybrqnd.com\u201d to trick users into thinking it\u2019s \u201cmybrand.com,\u201d our engine sees the similarity and flags it.<\/p>\n

This approach is especially effective against typosquatting, where attackers depend on people not noticing small changes.<\/p>\n

2. Using Data to Profile Domains<\/strong><\/h4>\n

We don\u2019t just look at what a domain says. We analyze how it\u2019s built.<\/p>\n

Our ML pulls together several data points, including:<\/p>\n