# PARI: A Probabilistic Approach to AS Relationships Inference

## TL;DR

This paper introduces PARI, a probabilistic algorithm for inferring AS relationships that explicitly models uncertainty and interdependence, improving understanding of inference reliability.

## Contribution

It presents a new paradigm for uncertainty modeling in AS relationship inference and implements it in the PARI algorithm, addressing gaps in prior deterministic methods.

## Key findings

- PARI effectively captures uncertainty in AS relationship inference.
- The approach improves the reliability of inferred relationships.
- Interdependence modeling enhances inference accuracy.

## Abstract

Over the last two decades, several algorithms have been proposed to infer the type of relationship between Autonomous Systems (ASes). While the recent works have achieved increasingly higher accuracy, there has not been a systematic study on the uncertainty of AS relationship inference. In this paper, we analyze the factors contributing to this uncertainty and introduce a new paradigm to explicitly model the uncertainty and reflect it in the inference result. We also present PARI, an exemplary algorithm implementing this paradigm, that leverages a novel technique to capture the interdependence of relationship inference across AS links.