Iris: Expressive Traffic Analysis for the Modern Internet

Thea Rossman, Diana Qing, Gerry Wan, and Zakir Durumeric, Stanford University

In this work, we investigate the needs of modern traffic analysis, and we introduce Iris, a framework for efficiently building complex, high-performance traffic analysis applications. Iris's key contribution is a compiler that transforms user-defined traffic filters, stream transformations, and computation written in Rust into an optimized processing pipeline. The Iris compiler eliminates redundant logic across analysis tasks to generate a unified runtime that minimizes aggregate work, allowing it to scale to hundreds of concurrent workloads. Rather than restricting users to a domain-specific query language, Iris provides a flexible development environment by exposing connection- and application-layer semantics as Rust data types to user-defined functions. We show that Iris can execute hundreds of analysis tasks concurrently at 100Gbps+ on a single commodity server, and we demonstrate its flexibility through three use cases drawn from prior work.

NSDI '26 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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BibTeX
@inproceedings {316692,
author = {Thea Rossman and Diana Qing and Gerry Wan and Zakir Durumeric},
title = {Iris: Expressive Traffic Analysis for the Modern Internet},
booktitle = {23rd USENIX Symposium on Networked Systems Design and Implementation (NSDI 26)},
year = {2026},
isbn = {978-1-939133-54-0},
address = {Renton, WA},
pages = {1153--1169},
url = {https://www.usenix.org/conference/nsdi26/presentation/rossman},
publisher = {USENIX Association},
month = may
}

Presentation Video