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Updates

  • (August 8, 2023) Added annotator reasoning for a subset of examples of security classification.
  • (Initial Release) The pretrained models are now available for download.

SPEC5G

This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" which is accepted in AACL 2023.

SPEC5G is a dataset for the analysis of natural language specification of 5G Cellular network protocol specification. SPEC5G contains 3,547,587 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on ML-based natural language processing (NLP) tasks, we have used this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task.

SPEC5G is the first-ever public 5G dataset for NLP research on network security.

Table of Contents

Datasets

Download the dataset from here. This includes:

  • Our original 134M Word training corpus (Gold_5G_v4.0.zip)
  • 5GSum - Summarization Dataset (simplification_dataset.csv)
  • 5GSC - Classification Dataset (5GSC.csv)
  • 5GSC Annotator Reasoning - Annotator Explanation for Subset of 5GSC

Models

The pretrained model checkpoints can be found below:

Dependencies

Training & Evaluation

Citation

If you use this dataset, models, or code modules, please cite the following paper:

@InProceedings{karim-EtAl:2023:findings,
author = {Karim, Imtiaz and Mubasshir, Kazi Samin and Rahman, Mirza Masfiqur and Bertino, Elisa},
title = {SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis},
booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
month = {November},
year = {2023},
address = {Nusa Dua, Bali},
publisher = {Association for Computational Linguistics},
pages = {20--38},
url = {https://aclanthology.org/2023.findings-ijcnlp.3}
}

About

This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" published at AACL 2023.

arxiv.org/abs/2301.09201

Topics

5glarge-datasetlarge-language-models

Resources

Readme
Apache-2.0 license
Activity
Custom properties

Stars

42 stars

Watchers

3 watching

Forks

11 forks
Report repository

Releases

Packages

Contributors

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Updates
  • (August 8, 2023) Added annotator reasoning for a subset of examples of security classification.
  • (Initial Release) The pretrained models are now available for download.
SPEC5G
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" which is accepted in AACL 2023.
SPEC5G is a dataset for the analysis of natural language specification of 5G Cellular network protocol specification. SPEC5G contains 3,547,587 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on ML-based natural language processing (NLP) tasks, we have used this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task.
SPEC5G is the first-ever public 5G dataset for NLP research on network security.
Table of Contents
Datasets
Download the dataset from here. This includes:
  • Our original 134M Word training corpus (Gold_5G_v4.0.zip)
  • 5GSum - Summarization Dataset (simplification_dataset.csv)
  • 5GSC - Classification Dataset (5GSC.csv)
  • 5GSC Annotator Reasoning - Annotator Explanation for Subset of 5GSC
Models
The pretrained model checkpoints can be found below:
Dependencies
Training & Evaluation
Citation
If you use this dataset, models, or code modules, please cite the following paper:
@InProceedings{karim-EtAl:2023:findings,
author = {Karim, Imtiaz and Mubasshir, Kazi Samin and Rahman, Mirza Masfiqur and Bertino, Elisa},
title = {SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis},
booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
month = {November},
year = {2023},
address = {Nusa Dua, Bali},
publisher = {Association for Computational Linguistics},
pages = {20--38},
url = {https://aclanthology.org/2023.findings-ijcnlp.3}
}

About
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" published at AACL 2023.
arxiv.org/abs/2301.09201
Topics
5glarge-datasetlarge-language-models
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
42 stars
Watchers
3 watching
Forks
11 forks
Report repository
Releases
Packages
Contributors
Languages

, '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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Updates
  • (August 8, 2023) Added annotator reasoning for a subset of examples of security classification.
  • (Initial Release) The pretrained models are now available for download.
SPEC5G
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" which is accepted in AACL 2023.
SPEC5G is a dataset for the analysis of natural language specification of 5G Cellular network protocol specification. SPEC5G contains 3,547,587 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on ML-based natural language processing (NLP) tasks, we have used this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task.
SPEC5G is the first-ever public 5G dataset for NLP research on network security.
Table of Contents
Datasets
Download the dataset from here. This includes:
  • Our original 134M Word training corpus (Gold_5G_v4.0.zip)
  • 5GSum - Summarization Dataset (simplification_dataset.csv)
  • 5GSC - Classification Dataset (5GSC.csv)
  • 5GSC Annotator Reasoning - Annotator Explanation for Subset of 5GSC
Models
The pretrained model checkpoints can be found below:
Dependencies
Training & Evaluation
Citation
If you use this dataset, models, or code modules, please cite the following paper:
@InProceedings{karim-EtAl:2023:findings,
author = {Karim, Imtiaz and Mubasshir, Kazi Samin and Rahman, Mirza Masfiqur and Bertino, Elisa},
title = {SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis},
booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
month = {November},
year = {2023},
address = {Nusa Dua, Bali},
publisher = {Association for Computational Linguistics},
pages = {20--38},
url = {https://aclanthology.org/2023.findings-ijcnlp.3}
}

About
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" published at AACL 2023.
arxiv.org/abs/2301.09201
Topics
5glarge-datasetlarge-language-models
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
42 stars
Watchers
3 watching
Forks
11 forks
Report repository
Releases
Packages
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Updates
  • (August 8, 2023) Added annotator reasoning for a subset of examples of security classification.
  • (Initial Release) The pretrained models are now available for download.
SPEC5G
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" which is accepted in AACL 2023.
SPEC5G is a dataset for the analysis of natural language specification of 5G Cellular network protocol specification. SPEC5G contains 3,547,587 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on ML-based natural language processing (NLP) tasks, we have used this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task.
SPEC5G is the first-ever public 5G dataset for NLP research on network security.
Table of Contents
Datasets
Download the dataset from here. This includes:
  • Our original 134M Word training corpus (Gold_5G_v4.0.zip)
  • 5GSum - Summarization Dataset (simplification_dataset.csv)
  • 5GSC - Classification Dataset (5GSC.csv)
  • 5GSC Annotator Reasoning - Annotator Explanation for Subset of 5GSC
Models
The pretrained model checkpoints can be found below:
Dependencies
Training & Evaluation
Citation
If you use this dataset, models, or code modules, please cite the following paper:
@InProceedings{karim-EtAl:2023:findings,
author = {Karim, Imtiaz and Mubasshir, Kazi Samin and Rahman, Mirza Masfiqur and Bertino, Elisa},
title = {SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis},
booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
month = {November},
year = {2023},
address = {Nusa Dua, Bali},
publisher = {Association for Computational Linguistics},
pages = {20--38},
url = {https://aclanthology.org/2023.findings-ijcnlp.3}
}

About
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" published at AACL 2023.
arxiv.org/abs/2301.09201
Topics
5glarge-datasetlarge-language-models
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
42 stars
Watchers
3 watching
Forks
11 forks
Report repository
Releases
Packages
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
Repository files navigation
Updates
  • (August 8, 2023) Added annotator reasoning for a subset of examples of security classification.
  • (Initial Release) The pretrained models are now available for download.
SPEC5G
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" which is accepted in AACL 2023.
SPEC5G is a dataset for the analysis of natural language specification of 5G Cellular network protocol specification. SPEC5G contains 3,547,587 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on ML-based natural language processing (NLP) tasks, we have used this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task.
SPEC5G is the first-ever public 5G dataset for NLP research on network security.
Table of Contents
Datasets
Download the dataset from here. This includes:
  • Our original 134M Word training corpus (Gold_5G_v4.0.zip)
  • 5GSum - Summarization Dataset (simplification_dataset.csv)
  • 5GSC - Classification Dataset (5GSC.csv)
  • 5GSC Annotator Reasoning - Annotator Explanation for Subset of 5GSC
Models
The pretrained model checkpoints can be found below:
Dependencies
Training & Evaluation
Citation
If you use this dataset, models, or code modules, please cite the following paper:
@InProceedings{karim-EtAl:2023:findings,
author = {Karim, Imtiaz and Mubasshir, Kazi Samin and Rahman, Mirza Masfiqur and Bertino, Elisa},
title = {SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis},
booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
month = {November},
year = {2023},
address = {Nusa Dua, Bali},
publisher = {Association for Computational Linguistics},
pages = {20--38},
url = {https://aclanthology.org/2023.findings-ijcnlp.3}
}

About
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" published at AACL 2023.
arxiv.org/abs/2301.09201
Topics
5glarge-datasetlarge-language-models
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
42 stars
Watchers
3 watching
Forks
11 forks
Report repository
Releases
Packages
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Updates
  • (August 8, 2023) Added annotator reasoning for a subset of examples of security classification.
  • (Initial Release) The pretrained models are now available for download.
SPEC5G
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" which is accepted in AACL 2023.
SPEC5G is a dataset for the analysis of natural language specification of 5G Cellular network protocol specification. SPEC5G contains 3,547,587 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on ML-based natural language processing (NLP) tasks, we have used this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task.
SPEC5G is the first-ever public 5G dataset for NLP research on network security.
Table of Contents
Datasets
Download the dataset from here. This includes:
  • Our original 134M Word training corpus (Gold_5G_v4.0.zip)
  • 5GSum - Summarization Dataset (simplification_dataset.csv)
  • 5GSC - Classification Dataset (5GSC.csv)
  • 5GSC Annotator Reasoning - Annotator Explanation for Subset of 5GSC
Models
The pretrained model checkpoints can be found below:
Dependencies
Training & Evaluation
Citation
If you use this dataset, models, or code modules, please cite the following paper:
@InProceedings{karim-EtAl:2023:findings,
author = {Karim, Imtiaz and Mubasshir, Kazi Samin and Rahman, Mirza Masfiqur and Bertino, Elisa},
title = {SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis},
booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
month = {November},
year = {2023},
address = {Nusa Dua, Bali},
publisher = {Association for Computational Linguistics},
pages = {20--38},
url = {https://aclanthology.org/2023.findings-ijcnlp.3}
}

About
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" published at AACL 2023.
arxiv.org/abs/2301.09201
Topics
5glarge-datasetlarge-language-models
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
42 stars
Watchers
3 watching
Forks
11 forks
Report repository
Releases
Packages
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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Updates
  • (August 8, 2023) Added annotator reasoning for a subset of examples of security classification.
  • (Initial Release) The pretrained models are now available for download.
SPEC5G
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" which is accepted in AACL 2023.
SPEC5G is a dataset for the analysis of natural language specification of 5G Cellular network protocol specification. SPEC5G contains 3,547,587 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on ML-based natural language processing (NLP) tasks, we have used this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task.
SPEC5G is the first-ever public 5G dataset for NLP research on network security.
Table of Contents
Datasets
Download the dataset from here. This includes:
  • Our original 134M Word training corpus (Gold_5G_v4.0.zip)
  • 5GSum - Summarization Dataset (simplification_dataset.csv)
  • 5GSC - Classification Dataset (5GSC.csv)
  • 5GSC Annotator Reasoning - Annotator Explanation for Subset of 5GSC
Models
The pretrained model checkpoints can be found below:
Dependencies
Training & Evaluation
Citation
If you use this dataset, models, or code modules, please cite the following paper:
@InProceedings{karim-EtAl:2023:findings,
author = {Karim, Imtiaz and Mubasshir, Kazi Samin and Rahman, Mirza Masfiqur and Bertino, Elisa},
title = {SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis},
booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
month = {November},
year = {2023},
address = {Nusa Dua, Bali},
publisher = {Association for Computational Linguistics},
pages = {20--38},
url = {https://aclanthology.org/2023.findings-ijcnlp.3}
}

About
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" published at AACL 2023.
arxiv.org/abs/2301.09201
Topics
5glarge-datasetlarge-language-models
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
42 stars
Watchers
3 watching
Forks
11 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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Updates
  • (August 8, 2023) Added annotator reasoning for a subset of examples of security classification.
  • (Initial Release) The pretrained models are now available for download.
SPEC5G
This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" which is accepted in AACL 2023.
SPEC5G is a dataset for the analysis of natural language specification of 5G Cellular network protocol specification. SPEC5G contains 3,547,587 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on ML-based natural language processing (NLP) tasks, we have used this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task.
SPEC5G is the first-ever public 5G dataset for NLP research on network security.
Table of Contents
Datasets
Download the dataset from here. This includes:
  • Our original 134M Word training corpus (Gold_5G_v4.0.zip)
  • 5GSum - Summarization Dataset (simplification_dataset.csv)
  • 5GSC - Classification Dataset (5GSC.csv)
  • 5GSC Annotator Reasoning - Annotator Explanation for Subset of 5GSC
Models
The pretrained model checkpoints can be found below:
Dependencies
Training & Evaluation
Citation
If you use this dataset, models, or code modules, please cite the following paper:
@InProceedings{karim-EtAl:2023:findings,
author = {Karim, Imtiaz and Mubasshir, Kazi Samin and Rahman, Mirza Masfiqur and Bertino, Elisa},
title = {SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis},
booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
month = {November},
year = {2023},
address = {Nusa Dua, Bali},
publisher = {Association for Computational Linguistics},
pages = {20--38},
url = {https://aclanthology.org/2023.findings-ijcnlp.3}
}

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This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" published at AACL 2023.
arxiv.org/abs/2301.09201
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5glarge-datasetlarge-language-models
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