Development of a machine learning model that identifies lookalike domains by analyzing how characters visually appear to humans, rather than just comparing characters directly.
Lookalike Detection
Training an LLM to detect lookalike domains to thwart attempted vendor fraud.
What is the item?
Utilization of advanced techniques like pixel-based encoding and Levenshtein distance to detect subtle visual similarities between characters across Unicode and non-English character sets.
Why is it helpful to our customers?
Protects customers from phishing and vendor fraud attacks by accurately identifying deceptive domains that are visually similar to legitimate ones.
Reduces reliance on human vigilance to spot subtle differences in domains, significantly lowering the risk of falling victim to impersonation-based attacks.
Why is it interesting?
This innovation bridges human visual perception with machine learning, enabling detection of threats that are nearly indistinguishable to the human eye.
The approach evolves beyond traditional edit-distance metrics, showcasing how AI can creatively address the limitations of manual or rule-based systems.