USPTO Machine Learning DNS Monitoring Patent Application

ChangeBridge: Patent Apps - Business Methods (G06Q)

Detected March 23rd, 2026

Summary

The USPTO has published a patent application (US20260081909A1) detailing a machine learning-based approach for monitoring DNS activity to detect computer security anomalies. The application, assigned to SWOOP IP HOLDINGS LLC, describes a system that uses historical behavior data to identify deviations from predictable request patterns.

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What changed

USPTO patent application US20260081909A1, filed on November 26, 2025, by SWOOP IP HOLDINGS LLC, describes a novel method for enhancing computer security through machine learning and DNS monitoring. The proposed system analyzes historical DNS behavior data for entities like customers and vendors to establish a baseline of predictable requests. A machine learning algorithm then compares new requests against this baseline to detect anomalies, triggering confirmation messages before authorizing secure operations.

This patent application, while not a regulation, signals potential future technological advancements in cybersecurity. Companies developing or utilizing DNS monitoring and anomaly detection systems, particularly those incorporating machine learning, should be aware of this patented approach. While there are no immediate compliance obligations, understanding such innovations can inform internal security strategies and R&D efforts.

Source document (simplified)

← USPTO Patent Applications

MACHINE-LEARNING BASED DNS FIDELITY MONITORING AND BEHAVIORAL ANOMALY DETECTION FOR COMPUTER SECURITY

Application US20260081909A1 Kind: A1 Mar 19, 2026

Assignee

SWOOP IP HOLDINGS LLC

Inventors

John P. KILLORAN, Jr., Graham BASS

Abstract

An approach for improving computer security using machine learning and DNS-based monitoring. A server receives requests associated with multiple entities, including at least customers and vendors, and maintains historical behavior data comprising prior requests, corresponding outcomes, and DNS record information for associated domains. A machine learning algorithm determines a range of predictable requests for a given entity based on the historical behavior data and compares new requests to this range to detect anomalies or behavior inconsistent with past activity. When an anomaly is detected, the server causes one or more confirmation messages to be sent, for example via email, SMS, social media messaging, instant messaging, or application notifications, before authorizing a secure operation.