DNS-Based Domain Name Ranking Using Query Metrics
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Solution Overview
Problem
Traditional website ranking methods fail to account for subdomain names, non-web communication domain names, network security issues, and are limited in scope, often ranking only a limited number of websites and being vulnerable to malicious activities.
Innovation Solution
A method and system for ranking domain names based on DNS data, including Fully Qualified Domain Names (FQDNs), core domain names, and subdomain names, using metric values such as query count, client count, and network count, with filtering to remove malicious activity and normalization to provide percentile ranks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional website ranking methods are used, then ranking of core domain names is achieved, but subdomain names and non-web communication domain names are not accounted for
Solution Approach 1:
The DNS-based ranking system is designed to handle multiple types of domain names universally, including core domain names, subdomain names, and domain names associated with non-web services (APIs, mobile applications, IoT devices). The system processes DNS query data without discrimination based on domain type, making the ranking methodology multi-functional and applicable to the entire domain name space rather than being limited to traditional web websites only.
2Adaptability or versatility
If traditional ranking methods focus on human-readable website pages, then web site popularity is measured, but domain names associated with web services and APIs without web pages are not ranked
Solution Approach 1:
The ranking system extends beyond human-readable web pages to encompass all domain names that generate DNS queries, including those for web services, APIs, mobile applications, and IoT devices. The methodology is agnostic to the specific type of service or application, ranking domain names based purely on DNS query patterns, thus universally covering the entire spectrum of networked services.
3Quantity of substance
If traditional ranking methods are used, then limited number of website pages are ranked, but comprehensive global DNS traffic analysis is not performed
Solution Approach 1:
The system extracts and processes only the essential elements needed for ranking from the vast global DNS traffic data - specifically DNS query counts, client counts, and network counts. By focusing on these extracted key metrics rather than attempting to analyze all DNS data in its entirety, the system can handle comprehensive global traffic while maintaining manageable processing complexity.
Solution Approach 2:
The system performs preliminary aggregation of DNS data at multiple levels (individual DNS servers, ISPs, and globally) before final ranking computation. This preliminary processing organizes the massive DNS traffic data into structured metrics (query counts, client counts, network counts) that can be efficiently processed for ranking, reducing the complexity of the final analysis step.
4Reliability
If traditional ranking methods are used, then website popularity is measured, but vulnerability to malicious activities is not addressed
Solution Approach 1:
The system applies preliminary filtering and analysis to DNS data to identify and mitigate the impact of malicious activities before they can skew ranking results. By detecting patterns associated with malware propagation, phishing, and botnet communications, the system can exclude or separately analyze these harmful domain names, preventing them from artificially inflating or distorting the popularity measurements of legitimate domains.
Data Source
AI summary
Provided is a method for domain name ranking. An example method includes receiving Domain Name System (DNS) data, which includes domain names. The DNS data is processed to obtain multiple metric values for each of the domain names. The metric values can include a query count (QC), a client count (CC), and a network count (NC). The method proceeds with calculating a score for each of the domain names based on the metric values. The calculation can be performed using the following equation: Score=NC·CC·(1+log(QC)). Furthermore, the method ranks the domain names based on the score for each of the domain names. The ranking can be based on normalization of the scores or based on converting the scores into respective percentile ranks.