DNS Domain Ranking Using Bootstrap Confidence Intervals
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Solution Overview
Problem
Existing DNS rank lists are inherently unstable due to factors like network noise, external influences, and natural variance, leading to inaccurate domain rankings that can impact DNS security and optimization systems.
Innovation Solution
A statistical inference technique using bootstrap sampling and confidence intervals to analyze DNS query data, generating a 'Technical Application' statistically significant rank lists that provide both a range of plausible ranks for a given item, as well as the most likely rank during a given time period within the observations. This approach allows domains to be accurately ranked, thereby increasing the confidence of its most likely rank.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional DNS rank lists are generated using simple counting methods, then the generation process is simple and fast, but the ranking accuracy and stability deteriorate due to network noise and natural variance
Solution Approach 1:
The patent applies preliminary action by performing bootstrap sampling and statistical analysis in advance to establish confidence intervals and most likely ranks before final ranking decisions are made. This pre-processing of data through resampling techniques prepares the foundation for more accurate and stable rankings, addressing the accuracy-stability problem before the actual ranking occurs.
Solution Approach 2:
The patent introduces statistical inference techniques and confidence intervals as intermediary layers between raw DNS query counts and final domain rankings. These statistical methods act as mediators that filter out network noise and natural variance, transforming unstable raw data into stable, accurate rankings while managing the complexity through structured statistical frameworks.
2Reliability
If bootstrap sampling and confidence intervals are used to improve ranking stability, then the reliability of rankings improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by implementing bootstrap sampling with a controlled number of iterations (e.g., 1000 iterations) rather than exhaustive sampling. This provides sufficient statistical reliability for practical purposes while avoiding the diminishing returns of excessive computation. The confidence interval calculations are performed on sampled data rather than complete datasets, balancing reliability with processing time constraints.
3Measurement precision
If statistical inference techniques are applied to reduce the impact of network noise, then the measurement precision improves, but the computational resources required increase
Solution Approach 1:
The patent uses copying through bootstrap sampling, where multiple resampled datasets are created as copies of the original DNS query data. These synthetic copies allow statistical analysis to be performed on replicated data structures, enabling precise ranking measurements without requiring additional physical computational resources beyond the initial data processing. The resampled copies serve as virtual representations that consume minimal additional energy.
Data Source
AI summary
Techniques for ranking services and top N rank lists are disclosed. In some embodiments, a system, process, and/or computer program product for ranking services and top N rank lists includes receiving a set of network related event data, wherein the set of network related event data includes Domain Name System (DNS) related event data; aggregating the DNS related event data over a period of time and rank order by popularity; and generating a top N rank list for ranking popularity over the period of time for a set of domains using the aggregated DNS related event data and rank order by popularity.