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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.

About

Sampling and benchmarking code for comparing IPID selection methods' correctness, security, and performance

Topics

benchmarkip-identifiersnetwork-securityprobability-theory

Resources

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MIT license
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
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.
About
Sampling and benchmarking code for comparing IPID selection methods' correctness, security, and performance
Topics
benchmarkip-identifiersnetwork-securityprobability-theory
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
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.
About
Sampling and benchmarking code for comparing IPID selection methods' correctness, security, and performance
Topics
benchmarkip-identifiersnetwork-securityprobability-theory
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
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.
About
Sampling and benchmarking code for comparing IPID selection methods' correctness, security, and performance
Topics
benchmarkip-identifiersnetwork-securityprobability-theory
Resources
Readme
MIT license
Activity
Custom properties
Stars
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
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.
About
Sampling and benchmarking code for comparing IPID selection methods' correctness, security, and performance
Topics
benchmarkip-identifiersnetwork-securityprobability-theory
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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README.md
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uv.lock
Repository files navigation
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.
About
Sampling and benchmarking code for comparing IPID selection methods' correctness, security, and performance
Topics
benchmarkip-identifiersnetwork-securityprobability-theory
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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README.md
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pyproject.toml
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uv.lock
Repository files navigation
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.
About
Sampling and benchmarking code for comparing IPID selection methods' correctness, security, and performance
Topics
benchmarkip-identifiersnetwork-securityprobability-theory
Resources
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MIT license
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injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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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.
About
Sampling and benchmarking code for comparing IPID selection methods' correctness, security, and performance
Topics
benchmarkip-identifiersnetwork-securityprobability-theory
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Contributors
Languages