Mapping Internet Backbone Traffic for Understanding Communication Policy Issues: Surveillance and Network Sovereignty in a North American Context
Bibliographic record
Abstract
Objectives: There is much attention to internet policy issues but this is mainly focused on activities at the ‘edges’ of the internet. Furthermore, understanding internet traffic routing and storage issues and their associated policy implications is made more difficult by how the ‘cloud’ metaphor obscure the ‘hard’ structures, jurisdictional boundaries and dynamic processes of internet routing. This paper reports on research aimed at rendering more visible and amenable to public policy treatment the relatively hidden aspects of backbone routing. By mapping the routes packets take across the North America, this research seeks to shed light on two phenomena in particular: internet surveillance conducted by security and law enforcement agencies (eg NSA, CSE); and ‘boomerang routing’, where packets originating and terminating in the same country are routed via one or more other countries where they may be subject to surveillance or delay. These have implications for privacy and ‘network sovereignty.’ Methods: There are several steps in the analysis and mapping of internet routing: 1) crowdsourced generation of traceroutes - user installed traceroute generation software which automatically ‘pings’ a series of predefined destination URLs and uploads results to central IXmaps database; 2) geo-location of IP addresses produced in 1) assigning lat/long to IP addresses via a combination of three techniques: traceroute-landmark; hostname parsing; and comparative hop latency; 3) background research on the carriers, data centers and exchange points referenced in traceroutes; 4) combining data from previous steps by mapping traceroutes and intermediate sites in Google Earth.Data: So far our database contains over 14,000 individual traceroutes spanning North America, based on 92 originating addresses, over 1,700 destination URLs: 1722 and includes more than 13,000 unique IP Addresses, half of which have been geo-located to the city level or better. Novelty: Prior work in mapping internet traffic typically shows aggregate flows between major switching centers from the perspective of the carriers. The mapping of individual routes between user selected origins and destination provides for a much finer grained analysis and more specific policy insights. eg. We show that a significant portion of intra-Canadian traffic transits via the US. Furthermore, it enables a nuanced account for why particular routes cross borders or pass through surveillance sites, as well as suggesting remedial responses such as the siting of backbone facilities and jurisdictional regulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".