Domain Name Inference for IDN Homograph Attack Detection
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Solution Overview
Problem
Current methods for detecting IDN homograph attacks, which involve domain names visually similar to legitimate ones, are inadequate as they only handle partial character replacements and fail to accurately infer the likelihood of user recognition, limiting their effectiveness in identifying potentially malicious domain names.
Innovation Solution
An inference method that calculates similarities between a domain name and a list of legitimate domain names using structural similarity indices and external evaluation information, along with a training model to infer the probability of user recognition, enabling detection of both partial and complete visual similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structural similarity index (SSIM) or OCR methods are used to detect IDN homograph attacks, then detection capability for partial character replacements is improved, but the ability to accurately infer user recognition likelihood is insufficient
Solution Approach 1:
The detection process is segmented into multiple independent feature extraction components: visual similarity features (SSIM), character-level features (OCR), domain structure features, and lexical features. Each segment extracts specific aspects of domain name similarity, and their results are integrated to form a comprehensive detection judgment, thereby overcoming the limitation of single-method detection.
Solution Approach 2:
A machine learning classifier serves as an intermediary that bridges the gap between raw feature extraction and final detection judgment. The classifier takes multiple feature types as input and outputs a probabilistic assessment of user recognition likelihood, transforming incomplete detection signals into actionable security decisions.
2Measurement precision
If multiple feature types and machine learning models are integrated, then user recognition likelihood inference is improved, but system complexity increases
Solution Approach 1:
The complex system is segmented into modular functional components: feature extraction module (with sub-modules for visual, character, structural, and lexical features), model training module, and inference module. Each module has a specific responsibility and can be independently developed, tested, and optimized, reducing overall system complexity while maintaining high inference accuracy.
Solution Approach 2:
The system employs a universal machine learning framework that can handle multiple feature types and domain name variations through a single trained model. This multi-functional approach avoids the need for separate detection systems for different attack patterns, simplifying the overall architecture while maintaining comprehensive detection capability.
Data Source
AI summary
An inference method includes acquiring similarities between a domain name serving as an analysis object and each domain name indicated in a legitimate domain name list as feature amounts, and inferring a degree that the domain name serving as the analysis object is wrongly recognized as a legitimate domain name based on the feature amounts acquired at the acquiring and a training model that outputs, as a response to input of the feature amounts, a degree that the domain name serving as the analysis object is wrongly recognized as the legitimate domain name, by processing circuitry.