UCS Uses New AI Model for URL Classification
Eman Khalid 9/2/2026 107
Static blacklists and basic keyword matching are no longer sufficient to stop modern web-based attacks. As cybercriminals use AI to generate evasive URLs, security systems must respond in kind. Today, we are excited to share how Eunomatix UCS is leveraging a new hybrid AI model to set a new benchmark in web safety.
Beyond Classical Machine Learning
Traditional ML models rely on "lexical features"—like the length of a URL or the presence of special characters. While effective for common spam, they often fail to catch sophisticated phishing attempts that look perfectly "normal" to a computer but malicious to a human. Our new model bridges this gap by incorporating Large Language Model (LLM) capabilities.
How the New Model Works
- Semantic Inferencing: Instead of just looking at the string of characters, UCS now "reads" the content of the page to understand its true intent, detecting subtle emotional manipulation often found in phishing.
- Hybrid Intelligence: We combine the speed of classical ML with the deep reasoning of LLMs. This ensures real-time protection without the high latency usually associated with complex AI.
- Continuous Learning Loop: If the model encounters a domain it hasn't seen before, it uses AI to generate a risk score instantly, which then feeds back into the global database to protect all users within seconds.
The Results: Zero-Day Dominance
By automating the detection of 500 million+ domains with this high-fidelity intelligence, UCS can identify malicious sites the moment they are created. This "zero-hour" detection is critical for stopping short-lived phishing campaigns that typically bypass traditional security filters before they are even reported.
Originally published by Eman Khalid. Leading the transition from reactive filters to proactive AI intelligence.