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Cellularlint

The repository presents the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).

Hardware Specifications

CellularLint was successfully run with the following hardware-

  • CPU: AMD Ryzen Threadripper PRO 5965WX (24 core, 3.8 GHz)
  • GPU: Nvidia RTX 3090 (24GB)
  • Memory: 64GB.
  • Additionally, we recommend 50 GB of available disk space. However, it may work with less.

OS & Software Specifications

  • CellularLint can be successfully run on Ubuntu 20.04 LTS and using python3. It should also run on Ubuntu 22.04 LTS and later stable versions.

Installation

Please follow these steps to set up the environment- (For all the steps, we assume the current directory is:cellularlint-codes)

  • Download the pretrained models from Zenodo and place them under the Pretrained Models/ directory.
  • Download the SNLI Train Dataset from Zenodo and place it under the Data/SNLI/ directory. The validation and test datasets are already there.
  • Run chmod 700 unpack.sh followed by ./unpack.sh to unpack the pretrained models in the correct way.
  • Run pip install -r requirements.txt to install the required packages. (Note: The requirements were generated using pip freeze and modified manually to consider only the required packages. If a package is missing or runs into a problem, you may remove the specific version number, and it should still work.)

Running the experiments

  1. The following experiment is to generate figures similar to Figure 3 and Figure 4 of the paper.

    From the main directory, run- python3 tokenizer_and_sim_matrix.py 4G and python3 tokenizer_and_sim_matrix.py 5G for 4G and 5G datasets, respectively. Each of these should generate one PDF and one PNG formatted image file (Thus, in total, 4 files are generated) in the main directory. The generated files are-

    • 4G_embedding_times.png,
    • heatmap_4G.pdf,
    • 5G_embedding_times.png, and
    • heatmap_5G.pdf.

    The PDF files can be compared to Figure 3 of the paper, and PNG files can be compared to Figure 4 of the paper.

  2. The following experiment is to generate the models' performance metrics.

    Follow these instructions to train and use the language models-

    • Run the notebooks sequentially in the following order. (If you are using Jupyter GUI, you may do Kernel>Restart & Run All for each of them)-

      • train_bert.ipynb,
      • train_roberta.ipynb,
      • train_xlnet.ipynb,
      • phase_train_bert.ipynb,
      • phase_train_roberta.ipynb, and
      • phase_train_xlnet.ipynb

    • From the eval/ directory, run python3 -W ignore eval.py. It should generate the metrics (See output_metrics.txt in the same directory) like Table 1 (one phase) of the paper.

Citation

If you use the code or dataset used here, please cite our paper:

@inproceedings {298168,
author = {Mirza Masfiqur Rahman and Imtiaz Karim and Elisa Bertino},
title = {{CellularLint}: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications},
booktitle = {33rd USENIX Security Symposium (USENIX Security 24)},
year = {2024},
isbn = {978-1-939133-44-1},
address = {Philadelphia, PA},
pages = {5215--5232},
url = {https://www.usenix.org/conference/usenixsecurity24/presentation/rahman},
publisher = {USENIX Association},
month = aug
}

About

The repository contains the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).

cellularlint.github.io

Resources

Readme
CC0-1.0 license
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Custom properties

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6 stars

Watchers

3 watching

Forks

6 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} 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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Cellularlint
The repository presents the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
Hardware Specifications
CellularLint was successfully run with the following hardware-
  • CPU: AMD Ryzen Threadripper PRO 5965WX (24 core, 3.8 GHz)
  • GPU: Nvidia RTX 3090 (24GB)
  • Memory: 64GB.
  • Additionally, we recommend 50 GB of available disk space. However, it may work with less.
OS & Software Specifications
  • CellularLint can be successfully run on Ubuntu 20.04 LTS and using python3. It should also run on Ubuntu 22.04 LTS and later stable versions.
Installation
Please follow these steps to set up the environment-
(For all the steps, we assume the current directory is:cellularlint-codes)
  • Download the pretrained models from Zenodo and place them under the Pretrained Models/ directory.
  • Download the SNLI Train Dataset from Zenodo and place it under the Data/SNLI/ directory. The validation and test datasets are already there.
  • Run chmod 700 unpack.sh followed by ./unpack.sh to unpack the pretrained models in the correct way.
  • Run pip install -r requirements.txt to install the required packages. (Note: The requirements were generated using pip freeze and modified manually to consider only the required packages. If a package is missing or runs into a problem, you may remove the specific version number, and it should still work.)
Running the experiments
  1. The following experiment is to generate figures similar to Figure 3 and Figure 4 of the paper.
    From the main directory, run-
python3 tokenizer_and_sim_matrix.py 4G
and
python3 tokenizer_and_sim_matrix.py 5G
for 4G and 5G datasets, respectively. Each of these should generate one PDF and one PNG formatted image file (Thus, in total, 4 files are generated) in the main directory. The generated files are-
    • 4G_embedding_times.png,
    • heatmap_4G.pdf,
    • 5G_embedding_times.png, and
    • heatmap_5G.pdf.
    The PDF files can be compared to Figure 3 of the paper, and PNG files can be compared to Figure 4 of the paper.
  2. The following experiment is to generate the models' performance metrics.
    Follow these instructions to train and use the language models-
    • Run the notebooks sequentially in the following order. (If you are using Jupyter GUI, you may do Kernel>Restart & Run All for each of them)-
      • train_bert.ipynb,
      • train_roberta.ipynb,
      • train_xlnet.ipynb,
      • phase_train_bert.ipynb,
      • phase_train_roberta.ipynb, and
      • phase_train_xlnet.ipynb
    • From the eval/ directory, run python3 -W ignore eval.py. It should generate the metrics (See output_metrics.txt in the same directory) like Table 1 (one phase) of the paper.
Citation
If you use the code or dataset used here, please cite our paper:
@inproceedings {298168,
author = {Mirza Masfiqur Rahman and Imtiaz Karim and Elisa Bertino},
title = {{CellularLint}: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications},
booktitle = {33rd USENIX Security Symposium (USENIX Security 24)},
year = {2024},
isbn = {978-1-939133-44-1},
address = {Philadelphia, PA},
pages = {5215--5232},
url = {https://www.usenix.org/conference/usenixsecurity24/presentation/rahman},
publisher = {USENIX Association},
month = aug
}

About
The repository contains the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
cellularlint.github.io
Resources
Readme
CC0-1.0 license
Activity
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Stars
6 stars
Watchers
3 watching
Forks
6 forks
Report repository
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Used by
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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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Cellularlint
The repository presents the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
Hardware Specifications
CellularLint was successfully run with the following hardware-
  • CPU: AMD Ryzen Threadripper PRO 5965WX (24 core, 3.8 GHz)
  • GPU: Nvidia RTX 3090 (24GB)
  • Memory: 64GB.
  • Additionally, we recommend 50 GB of available disk space. However, it may work with less.
OS & Software Specifications
  • CellularLint can be successfully run on Ubuntu 20.04 LTS and using python3. It should also run on Ubuntu 22.04 LTS and later stable versions.
Installation
Please follow these steps to set up the environment-
(For all the steps, we assume the current directory is:cellularlint-codes)
  • Download the pretrained models from Zenodo and place them under the Pretrained Models/ directory.
  • Download the SNLI Train Dataset from Zenodo and place it under the Data/SNLI/ directory. The validation and test datasets are already there.
  • Run chmod 700 unpack.sh followed by ./unpack.sh to unpack the pretrained models in the correct way.
  • Run pip install -r requirements.txt to install the required packages. (Note: The requirements were generated using pip freeze and modified manually to consider only the required packages. If a package is missing or runs into a problem, you may remove the specific version number, and it should still work.)
Running the experiments
  1. The following experiment is to generate figures similar to Figure 3 and Figure 4 of the paper.
    From the main directory, run-
python3 tokenizer_and_sim_matrix.py 4G
and
python3 tokenizer_and_sim_matrix.py 5G
for 4G and 5G datasets, respectively. Each of these should generate one PDF and one PNG formatted image file (Thus, in total, 4 files are generated) in the main directory. The generated files are-
    • 4G_embedding_times.png,
    • heatmap_4G.pdf,
    • 5G_embedding_times.png, and
    • heatmap_5G.pdf.
    The PDF files can be compared to Figure 3 of the paper, and PNG files can be compared to Figure 4 of the paper.
  2. The following experiment is to generate the models' performance metrics.
    Follow these instructions to train and use the language models-
    • Run the notebooks sequentially in the following order. (If you are using Jupyter GUI, you may do Kernel>Restart & Run All for each of them)-
      • train_bert.ipynb,
      • train_roberta.ipynb,
      • train_xlnet.ipynb,
      • phase_train_bert.ipynb,
      • phase_train_roberta.ipynb, and
      • phase_train_xlnet.ipynb
    • From the eval/ directory, run python3 -W ignore eval.py. It should generate the metrics (See output_metrics.txt in the same directory) like Table 1 (one phase) of the paper.
Citation
If you use the code or dataset used here, please cite our paper:
@inproceedings {298168,
author = {Mirza Masfiqur Rahman and Imtiaz Karim and Elisa Bertino},
title = {{CellularLint}: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications},
booktitle = {33rd USENIX Security Symposium (USENIX Security 24)},
year = {2024},
isbn = {978-1-939133-44-1},
address = {Philadelphia, PA},
pages = {5215--5232},
url = {https://www.usenix.org/conference/usenixsecurity24/presentation/rahman},
publisher = {USENIX Association},
month = aug
}

About
The repository contains the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
cellularlint.github.io
Resources
Readme
CC0-1.0 license
Activity
Custom properties
Stars
6 stars
Watchers
3 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
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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Cellularlint
The repository presents the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
Hardware Specifications
CellularLint was successfully run with the following hardware-
  • CPU: AMD Ryzen Threadripper PRO 5965WX (24 core, 3.8 GHz)
  • GPU: Nvidia RTX 3090 (24GB)
  • Memory: 64GB.
  • Additionally, we recommend 50 GB of available disk space. However, it may work with less.
OS & Software Specifications
  • CellularLint can be successfully run on Ubuntu 20.04 LTS and using python3. It should also run on Ubuntu 22.04 LTS and later stable versions.
Installation
Please follow these steps to set up the environment-
(For all the steps, we assume the current directory is:cellularlint-codes)
  • Download the pretrained models from Zenodo and place them under the Pretrained Models/ directory.
  • Download the SNLI Train Dataset from Zenodo and place it under the Data/SNLI/ directory. The validation and test datasets are already there.
  • Run chmod 700 unpack.sh followed by ./unpack.sh to unpack the pretrained models in the correct way.
  • Run pip install -r requirements.txt to install the required packages. (Note: The requirements were generated using pip freeze and modified manually to consider only the required packages. If a package is missing or runs into a problem, you may remove the specific version number, and it should still work.)
Running the experiments
  1. The following experiment is to generate figures similar to Figure 3 and Figure 4 of the paper.
    From the main directory, run-
python3 tokenizer_and_sim_matrix.py 4G
and
python3 tokenizer_and_sim_matrix.py 5G
for 4G and 5G datasets, respectively. Each of these should generate one PDF and one PNG formatted image file (Thus, in total, 4 files are generated) in the main directory. The generated files are-
    • 4G_embedding_times.png,
    • heatmap_4G.pdf,
    • 5G_embedding_times.png, and
    • heatmap_5G.pdf.
    The PDF files can be compared to Figure 3 of the paper, and PNG files can be compared to Figure 4 of the paper.
  2. The following experiment is to generate the models' performance metrics.
    Follow these instructions to train and use the language models-
    • Run the notebooks sequentially in the following order. (If you are using Jupyter GUI, you may do Kernel>Restart & Run All for each of them)-
      • train_bert.ipynb,
      • train_roberta.ipynb,
      • train_xlnet.ipynb,
      • phase_train_bert.ipynb,
      • phase_train_roberta.ipynb, and
      • phase_train_xlnet.ipynb
    • From the eval/ directory, run python3 -W ignore eval.py. It should generate the metrics (See output_metrics.txt in the same directory) like Table 1 (one phase) of the paper.
Citation
If you use the code or dataset used here, please cite our paper:
@inproceedings {298168,
author = {Mirza Masfiqur Rahman and Imtiaz Karim and Elisa Bertino},
title = {{CellularLint}: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications},
booktitle = {33rd USENIX Security Symposium (USENIX Security 24)},
year = {2024},
isbn = {978-1-939133-44-1},
address = {Philadelphia, PA},
pages = {5215--5232},
url = {https://www.usenix.org/conference/usenixsecurity24/presentation/rahman},
publisher = {USENIX Association},
month = aug
}

About
The repository contains the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
cellularlint.github.io
Resources
Readme
CC0-1.0 license
Activity
Custom properties
Stars
6 stars
Watchers
3 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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" + '

Skip to content
BranchesTags
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Cellularlint
The repository presents the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
Hardware Specifications
CellularLint was successfully run with the following hardware-
  • CPU: AMD Ryzen Threadripper PRO 5965WX (24 core, 3.8 GHz)
  • GPU: Nvidia RTX 3090 (24GB)
  • Memory: 64GB.
  • Additionally, we recommend 50 GB of available disk space. However, it may work with less.
OS & Software Specifications
  • CellularLint can be successfully run on Ubuntu 20.04 LTS and using python3. It should also run on Ubuntu 22.04 LTS and later stable versions.
Installation
Please follow these steps to set up the environment-
(For all the steps, we assume the current directory is:cellularlint-codes)
  • Download the pretrained models from Zenodo and place them under the Pretrained Models/ directory.
  • Download the SNLI Train Dataset from Zenodo and place it under the Data/SNLI/ directory. The validation and test datasets are already there.
  • Run chmod 700 unpack.sh followed by ./unpack.sh to unpack the pretrained models in the correct way.
  • Run pip install -r requirements.txt to install the required packages. (Note: The requirements were generated using pip freeze and modified manually to consider only the required packages. If a package is missing or runs into a problem, you may remove the specific version number, and it should still work.)
Running the experiments
  1. The following experiment is to generate figures similar to Figure 3 and Figure 4 of the paper.
    From the main directory, run-
python3 tokenizer_and_sim_matrix.py 4G
and
python3 tokenizer_and_sim_matrix.py 5G
for 4G and 5G datasets, respectively. Each of these should generate one PDF and one PNG formatted image file (Thus, in total, 4 files are generated) in the main directory. The generated files are-
    • 4G_embedding_times.png,
    • heatmap_4G.pdf,
    • 5G_embedding_times.png, and
    • heatmap_5G.pdf.
    The PDF files can be compared to Figure 3 of the paper, and PNG files can be compared to Figure 4 of the paper.
  2. The following experiment is to generate the models' performance metrics.
    Follow these instructions to train and use the language models-
    • Run the notebooks sequentially in the following order. (If you are using Jupyter GUI, you may do Kernel>Restart & Run All for each of them)-
      • train_bert.ipynb,
      • train_roberta.ipynb,
      • train_xlnet.ipynb,
      • phase_train_bert.ipynb,
      • phase_train_roberta.ipynb, and
      • phase_train_xlnet.ipynb
    • From the eval/ directory, run python3 -W ignore eval.py. It should generate the metrics (See output_metrics.txt in the same directory) like Table 1 (one phase) of the paper.
Citation
If you use the code or dataset used here, please cite our paper:
@inproceedings {298168,
author = {Mirza Masfiqur Rahman and Imtiaz Karim and Elisa Bertino},
title = {{CellularLint}: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications},
booktitle = {33rd USENIX Security Symposium (USENIX Security 24)},
year = {2024},
isbn = {978-1-939133-44-1},
address = {Philadelphia, PA},
pages = {5215--5232},
url = {https://www.usenix.org/conference/usenixsecurity24/presentation/rahman},
publisher = {USENIX Association},
month = aug
}

About
The repository contains the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
cellularlint.github.io
Resources
Readme
CC0-1.0 license
Activity
Custom properties
Stars
6 stars
Watchers
3 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
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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Cellularlint
The repository presents the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
Hardware Specifications
CellularLint was successfully run with the following hardware-
  • CPU: AMD Ryzen Threadripper PRO 5965WX (24 core, 3.8 GHz)
  • GPU: Nvidia RTX 3090 (24GB)
  • Memory: 64GB.
  • Additionally, we recommend 50 GB of available disk space. However, it may work with less.
OS & Software Specifications
  • CellularLint can be successfully run on Ubuntu 20.04 LTS and using python3. It should also run on Ubuntu 22.04 LTS and later stable versions.
Installation
Please follow these steps to set up the environment-
(For all the steps, we assume the current directory is:cellularlint-codes)
  • Download the pretrained models from Zenodo and place them under the Pretrained Models/ directory.
  • Download the SNLI Train Dataset from Zenodo and place it under the Data/SNLI/ directory. The validation and test datasets are already there.
  • Run chmod 700 unpack.sh followed by ./unpack.sh to unpack the pretrained models in the correct way.
  • Run pip install -r requirements.txt to install the required packages. (Note: The requirements were generated using pip freeze and modified manually to consider only the required packages. If a package is missing or runs into a problem, you may remove the specific version number, and it should still work.)
Running the experiments
  1. The following experiment is to generate figures similar to Figure 3 and Figure 4 of the paper.
    From the main directory, run-
python3 tokenizer_and_sim_matrix.py 4G
and
python3 tokenizer_and_sim_matrix.py 5G
for 4G and 5G datasets, respectively. Each of these should generate one PDF and one PNG formatted image file (Thus, in total, 4 files are generated) in the main directory. The generated files are-
    • 4G_embedding_times.png,
    • heatmap_4G.pdf,
    • 5G_embedding_times.png, and
    • heatmap_5G.pdf.
    The PDF files can be compared to Figure 3 of the paper, and PNG files can be compared to Figure 4 of the paper.
  2. The following experiment is to generate the models' performance metrics.
    Follow these instructions to train and use the language models-
    • Run the notebooks sequentially in the following order. (If you are using Jupyter GUI, you may do Kernel>Restart & Run All for each of them)-
      • train_bert.ipynb,
      • train_roberta.ipynb,
      • train_xlnet.ipynb,
      • phase_train_bert.ipynb,
      • phase_train_roberta.ipynb, and
      • phase_train_xlnet.ipynb
    • From the eval/ directory, run python3 -W ignore eval.py. It should generate the metrics (See output_metrics.txt in the same directory) like Table 1 (one phase) of the paper.
Citation
If you use the code or dataset used here, please cite our paper:
@inproceedings {298168,
author = {Mirza Masfiqur Rahman and Imtiaz Karim and Elisa Bertino},
title = {{CellularLint}: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications},
booktitle = {33rd USENIX Security Symposium (USENIX Security 24)},
year = {2024},
isbn = {978-1-939133-44-1},
address = {Philadelphia, PA},
pages = {5215--5232},
url = {https://www.usenix.org/conference/usenixsecurity24/presentation/rahman},
publisher = {USENIX Association},
month = aug
}

About
The repository contains the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
cellularlint.github.io
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CC0-1.0 license
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3 watching
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6 forks
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, '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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Cellularlint
The repository presents the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
Hardware Specifications
CellularLint was successfully run with the following hardware-
  • CPU: AMD Ryzen Threadripper PRO 5965WX (24 core, 3.8 GHz)
  • GPU: Nvidia RTX 3090 (24GB)
  • Memory: 64GB.
  • Additionally, we recommend 50 GB of available disk space. However, it may work with less.
OS & Software Specifications
  • CellularLint can be successfully run on Ubuntu 20.04 LTS and using python3. It should also run on Ubuntu 22.04 LTS and later stable versions.
Installation
Please follow these steps to set up the environment-
(For all the steps, we assume the current directory is:cellularlint-codes)
  • Download the pretrained models from Zenodo and place them under the Pretrained Models/ directory.
  • Download the SNLI Train Dataset from Zenodo and place it under the Data/SNLI/ directory. The validation and test datasets are already there.
  • Run chmod 700 unpack.sh followed by ./unpack.sh to unpack the pretrained models in the correct way.
  • Run pip install -r requirements.txt to install the required packages. (Note: The requirements were generated using pip freeze and modified manually to consider only the required packages. If a package is missing or runs into a problem, you may remove the specific version number, and it should still work.)
Running the experiments
  1. The following experiment is to generate figures similar to Figure 3 and Figure 4 of the paper.
    From the main directory, run-
python3 tokenizer_and_sim_matrix.py 4G
and
python3 tokenizer_and_sim_matrix.py 5G
for 4G and 5G datasets, respectively. Each of these should generate one PDF and one PNG formatted image file (Thus, in total, 4 files are generated) in the main directory. The generated files are-
    • 4G_embedding_times.png,
    • heatmap_4G.pdf,
    • 5G_embedding_times.png, and
    • heatmap_5G.pdf.
    The PDF files can be compared to Figure 3 of the paper, and PNG files can be compared to Figure 4 of the paper.
  2. The following experiment is to generate the models' performance metrics.
    Follow these instructions to train and use the language models-
    • Run the notebooks sequentially in the following order. (If you are using Jupyter GUI, you may do Kernel>Restart & Run All for each of them)-
      • train_bert.ipynb,
      • train_roberta.ipynb,
      • train_xlnet.ipynb,
      • phase_train_bert.ipynb,
      • phase_train_roberta.ipynb, and
      • phase_train_xlnet.ipynb
    • From the eval/ directory, run python3 -W ignore eval.py. It should generate the metrics (See output_metrics.txt in the same directory) like Table 1 (one phase) of the paper.
Citation
If you use the code or dataset used here, please cite our paper:
@inproceedings {298168,
author = {Mirza Masfiqur Rahman and Imtiaz Karim and Elisa Bertino},
title = {{CellularLint}: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications},
booktitle = {33rd USENIX Security Symposium (USENIX Security 24)},
year = {2024},
isbn = {978-1-939133-44-1},
address = {Philadelphia, PA},
pages = {5215--5232},
url = {https://www.usenix.org/conference/usenixsecurity24/presentation/rahman},
publisher = {USENIX Association},
month = aug
}

About
The repository contains the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
cellularlint.github.io
Resources
Readme
CC0-1.0 license
Activity
Custom properties
Stars
6 stars
Watchers
3 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
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Cellularlint
The repository presents the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
Hardware Specifications
CellularLint was successfully run with the following hardware-
  • CPU: AMD Ryzen Threadripper PRO 5965WX (24 core, 3.8 GHz)
  • GPU: Nvidia RTX 3090 (24GB)
  • Memory: 64GB.
  • Additionally, we recommend 50 GB of available disk space. However, it may work with less.
OS & Software Specifications
  • CellularLint can be successfully run on Ubuntu 20.04 LTS and using python3. It should also run on Ubuntu 22.04 LTS and later stable versions.
Installation
Please follow these steps to set up the environment-
(For all the steps, we assume the current directory is:cellularlint-codes)
  • Download the pretrained models from Zenodo and place them under the Pretrained Models/ directory.
  • Download the SNLI Train Dataset from Zenodo and place it under the Data/SNLI/ directory. The validation and test datasets are already there.
  • Run chmod 700 unpack.sh followed by ./unpack.sh to unpack the pretrained models in the correct way.
  • Run pip install -r requirements.txt to install the required packages. (Note: The requirements were generated using pip freeze and modified manually to consider only the required packages. If a package is missing or runs into a problem, you may remove the specific version number, and it should still work.)
Running the experiments
  1. The following experiment is to generate figures similar to Figure 3 and Figure 4 of the paper.
    From the main directory, run-
python3 tokenizer_and_sim_matrix.py 4G
and
python3 tokenizer_and_sim_matrix.py 5G
for 4G and 5G datasets, respectively. Each of these should generate one PDF and one PNG formatted image file (Thus, in total, 4 files are generated) in the main directory. The generated files are-
    • 4G_embedding_times.png,
    • heatmap_4G.pdf,
    • 5G_embedding_times.png, and
    • heatmap_5G.pdf.
    The PDF files can be compared to Figure 3 of the paper, and PNG files can be compared to Figure 4 of the paper.
  2. The following experiment is to generate the models' performance metrics.
    Follow these instructions to train and use the language models-
    • Run the notebooks sequentially in the following order. (If you are using Jupyter GUI, you may do Kernel>Restart & Run All for each of them)-
      • train_bert.ipynb,
      • train_roberta.ipynb,
      • train_xlnet.ipynb,
      • phase_train_bert.ipynb,
      • phase_train_roberta.ipynb, and
      • phase_train_xlnet.ipynb
    • From the eval/ directory, run python3 -W ignore eval.py. It should generate the metrics (See output_metrics.txt in the same directory) like Table 1 (one phase) of the paper.
Citation
If you use the code or dataset used here, please cite our paper:
@inproceedings {298168,
author = {Mirza Masfiqur Rahman and Imtiaz Karim and Elisa Bertino},
title = {{CellularLint}: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications},
booktitle = {33rd USENIX Security Symposium (USENIX Security 24)},
year = {2024},
isbn = {978-1-939133-44-1},
address = {Philadelphia, PA},
pages = {5215--5232},
url = {https://www.usenix.org/conference/usenixsecurity24/presentation/rahman},
publisher = {USENIX Association},
month = aug
}

About
The repository contains the code for the paper "CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications", published at the 33rd USENIX Security Symposium (2024).
cellularlint.github.io
Resources
Readme
CC0-1.0 license
Activity
Custom properties
Stars
6 stars
Watchers
3 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages