Incentivizing and Evaluating Internet-Wide Network Measurements

Abstract

The Internet s size is a primary challenge to researchers attempting to capture its properties. Inferences are therefore often based on available measurements, which may be biased due to the measurement process. We seek to understand the dependence of sampling methodology on two network measurement projects. We examine the potential of Mechanical Turk (MTurk) to guide the selection of samples by country and reward. As a proof-of-concept, we design an IPv6 adoption experiment disguised as a human intelligence task. Using 75 dollars, we obtain an IPv6 adoption estimate that differed by less than 3 percent of public estimates. From this initial success and analysis of the price sensitivity, we attempt a crowd-sourced approach to obtain representative measurements of Internet source address validation. However, this second experiment violated MTurk s terms of service. We therefore perform a per-country sampling analysis of nine years of existing source validation data from the Spoofer project. We conclude that conventional sampling methods do not properly characterize the data, primarily due to the changing nature of the underlying population during the collection period.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2014
Accession Number
ADA607791

Entities

People

  • Gokay Huz

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Computer Science
  • Data Sets
  • Databases
  • Information Science
  • Network Architecture
  • Network Protocols
  • Network Science
  • Operating Systems
  • Statistical Analysis
  • Statistical Samples
  • Statistical Sampling
  • Test Sets
  • Training
  • United States
  • Web Browsers
  • Websites

Fields of Study

  • Computer science

Readers

  • Aerosol Science/Aerosol Physics
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference