Web Domain Variant Categorization and Visualization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cybersecurity systems fail to effectively address ongoing and growing security threats to websites and users from phishing attacks and malicious software, as they lack comprehensive methods for categorizing and visualizing web domain lifecycles and potential maliciousness.
Innovation Solution
The system automatically determines domain variants based on similarity with a seed domain, categorizes them into lifecycle stages, and provides user interfaces for visualization, recommending acquisition, monitoring, or takedown based on maliciousness scores, using machine learning to assess potential threats and track domain activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive domain monitoring and categorization is implemented, then cybersecurity protection capability is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system segments domain monitoring into distinct lifecycle categories (unregistered, registered, active, dormant, taken-down) and further divides them into subcategories based on maliciousness levels. This segmentation allows comprehensive monitoring to be organized into manageable segments, reducing the perceived complexity while maintaining comprehensive coverage.
Solution Approach 2:
The patent introduces a new dimension of domain lifecycle staging alongside traditional monitoring approaches. By adding the temporal dimension of domain lifecycle (from unregistered to taken-down) and combining it with maliciousness scoring, the system creates a multi-dimensional framework that organizes complex data into structured categories, making the overall system more manageable.
2Measurement precision
If automated domain variant determination and categorization is implemented, then threat detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by automatically determining domain variants and pre-categorizing them into lifecycle stages and maliciousness categories before actual threats materialize. This preliminary categorization structure is established in advance, allowing for faster processing when actual threat assessment is needed, as the foundational framework is already in place.
Solution Approach 2:
The system changes parameters by introducing multiple scoring dimensions (maliciousness scores, lifecycle stage indicators, category classifications) that transform raw domain data into structured, categorized information. These parameter changes enable more precise threat detection while the automated nature of the parameter transformation reduces manual processing time.
3Reliability
If detailed domain lifecycle tracking is implemented, then security threat mitigation is improved, but data management complexity increases
Solution Approach 1:
Domain lifecycle tracking is segmented into distinct stages (unregistered, registered, active, dormant, taken-down) with clear transition criteria. Each stage has associated subcategories and maliciousness score ranges, creating a segmented data management structure that reduces complexity by organizing data into discrete, manageable segments rather than continuous unstructured data.
Solution Approach 2:
The patent introduces intermediary elements including maliciousness scores and category classifications that act as mediators between raw domain data and security threat assessments. These intermediaries structure the data in a standardized format, reducing data management complexity by providing a consistent framework for organizing and interpreting domain lifecycle information.
Data Source
AI summary
Systems and methods are disclosed for categorizing and visualizing web domain details. In implementations, one or more processors are configured to automatically determine domain variants, using a provided seed domain, based on a level of similarity with the seed domain. The one or more processors may be configured to categorize the domain variants into a plurality of categories. One or more servers may be communicatively coupled with one or more computing devices and may be configured to provide one or more user interfaces for display on the one or more computing devices. The one or more user interfaces may include a visual display of the categories and, for each category, an indicator indicating a total number of the domain variants within that category. Implementations may include training a machine learning module to automatically determine the domain variants and to categorize the domain variants into the plurality of categories.