A Comparative Analysis of IPID Selection Methods

This repository contains the supporting simulation, benchmarking, and plotting code for the paper A Taxonomy and Comparative Analysis of IPv4 ID Selection Correctness, Security, and Performance by Joshua J. Daymude, Antonio M. Espinoza, Holly Bergen, Benjamin Mixon–Baca, Jeffrey Knockel, and Jedidiah R. Crandall.

Getting Started

We use uv to manage Python environments. Install it and then run the following to get all dependencies:

uv sync

Then activate the corresponding virtual environment with:

source .venv/bin/activate

Many of our results involve computationally expensive sampling or benchmarking experiments. If you would like to reproduce our results from scratch, follow the instructions below. Otherwise, download our pre-computed results (available here) and extract them in this directory as results/. You can then call the corresponding plotting scripts in collisions.py (correctness), security.py (security), and benchplot.py (performance) without having to wait for the long runtimes.

Usage Guide

Correctness

collisions.py contains our correctness analysis (Section 4.2). Run it with

python collisions.py -P <num_cores>

where -P optionally specifies additional cores to speed up the calculations depending on sampling. Use python collisions.py --help for all options. If results/collisions/ already exists, it will use the results therein instead of calculating them from scratch.

Security

security.py contains our security analysis (Section 4.3). Run it with

python security.py -P <num_cores>

where -P optionally specifies additional cores to speed up the calculations depending on sampling. Use python security.py --help for all options. If results/security/ already exists, it will use the results therein instead of calculating them from scratch.

Performance

Our runtime benchmark (Section 4.4, Appendix B) is implemented in C++ and is found in the benchmark/ directory. If you downloaded our pre-computed results (in this case, results/benchmark/), then you can plot the outcome with python benchplot.py (use the --help option for details).

If you are trying to run the benchmark from scratch, navigate to benchmark/ and then build with ./build.sh. Any build errors likely will have to do with your C++ version (we require C++20), or missing boost libraries (we use boost::program_options). A successful build will create a build/ directory containing a variety of build artifacts.

View the executable's options using ./build/benchmark --help. Our benchmark can be replicated using ./run_local.sh, though you may need to adjust the parameters based on your hardware. For use on the ASU Sol supercomputer, use the run_sol.sh script instead.