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AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression

This repository contains all the digital artifacts associated with the paper "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."

Dataset

The dataset used in this work is available in the IFLforTFC repository. The notebook will automatically download it, but you can also obtain it manually:

  • Direct link:dataset.parquet
  • Contents: 3,163,462 IP flow records with 72 features and application labels for 10 traffic classes

Repository Structure

1. VanillaAE

This Jupyter Notebook contains all resources related to the Vanilla Autoencoder used in the paper. It includes the entire workflow:

  • Data Preprocessing: Includes steps like outlier removal and robust scaling.
  • Autoencoder Architecture: Details of the Vanilla Autoencoder model used for IP flow record compression.
  • Training Process: Training configuration, loss function, optimizer, and training duration.
  • Results Analysis: Contains visualizations and metrics for model performance.

2. VanillaAEvsDAE

This Jupyter Notebook provides a comparative analysis between the Vanilla Autoencoder and the Denoising Autoencoder (DAE).

How to Use

  1. Install dependencies:pip install torch scikit-learn pandas numpy matplotlib seaborn scipy joblib requests
  2. Run VanillaAE.ipynb: Downloads the dataset and reproduces all experiments and results from the paper.
  3. Run VanillaAEvsDAE.ipynb: Comparative analysis between Vanilla AE and Denoising AE. Requires dataset.parquet (produced by step 2).

Both notebooks include pre-computed cell outputs matching the final results reported in the paper.

About

Supporting page for the manuscript titled, "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."

Topics

autoencodercompressionip-flowmachine-learningnetwork-traffic-classificationsize-reduction

Resources

Readme
Activity
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Stars

1 star

Watchers

1 watching

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0 forks
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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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AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression
This repository contains all the digital artifacts associated with the paper "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Dataset
The dataset used in this work is available in the IFLforTFC repository. The notebook will automatically download it, but you can also obtain it manually:
  • Direct link:dataset.parquet
  • Contents: 3,163,462 IP flow records with 72 features and application labels for 10 traffic classes
Repository Structure
1. VanillaAE
This Jupyter Notebook contains all resources related to the Vanilla Autoencoder used in the paper. It includes the entire workflow:
  • Data Preprocessing: Includes steps like outlier removal and robust scaling.
  • Autoencoder Architecture: Details of the Vanilla Autoencoder model used for IP flow record compression.
  • Training Process: Training configuration, loss function, optimizer, and training duration.
  • Results Analysis: Contains visualizations and metrics for model performance.
2. VanillaAEvsDAE
This Jupyter Notebook provides a comparative analysis between the Vanilla Autoencoder and the Denoising Autoencoder (DAE).
How to Use
  1. Install dependencies:pip install torch scikit-learn pandas numpy matplotlib seaborn scipy joblib requests
  2. Run VanillaAE.ipynb: Downloads the dataset and reproduces all experiments and results from the paper.
  3. Run VanillaAEvsDAE.ipynb: Comparative analysis between Vanilla AE and Denoising AE. Requires dataset.parquet (produced by step 2).
Both notebooks include pre-computed cell outputs matching the final results reported in the paper.
About
Supporting page for the manuscript titled, "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Topics
autoencodercompressionip-flowmachine-learningnetwork-traffic-classificationsize-reduction
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
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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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AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression
This repository contains all the digital artifacts associated with the paper "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Dataset
The dataset used in this work is available in the IFLforTFC repository. The notebook will automatically download it, but you can also obtain it manually:
  • Direct link:dataset.parquet
  • Contents: 3,163,462 IP flow records with 72 features and application labels for 10 traffic classes
Repository Structure
1. VanillaAE
This Jupyter Notebook contains all resources related to the Vanilla Autoencoder used in the paper. It includes the entire workflow:
  • Data Preprocessing: Includes steps like outlier removal and robust scaling.
  • Autoencoder Architecture: Details of the Vanilla Autoencoder model used for IP flow record compression.
  • Training Process: Training configuration, loss function, optimizer, and training duration.
  • Results Analysis: Contains visualizations and metrics for model performance.
2. VanillaAEvsDAE
This Jupyter Notebook provides a comparative analysis between the Vanilla Autoencoder and the Denoising Autoencoder (DAE).
How to Use
  1. Install dependencies:pip install torch scikit-learn pandas numpy matplotlib seaborn scipy joblib requests
  2. Run VanillaAE.ipynb: Downloads the dataset and reproduces all experiments and results from the paper.
  3. Run VanillaAEvsDAE.ipynb: Comparative analysis between Vanilla AE and Denoising AE. Requires dataset.parquet (produced by step 2).
Both notebooks include pre-computed cell outputs matching the final results reported in the paper.
About
Supporting page for the manuscript titled, "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Topics
autoencodercompressionip-flowmachine-learningnetwork-traffic-classificationsize-reduction
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 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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AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression
This repository contains all the digital artifacts associated with the paper "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Dataset
The dataset used in this work is available in the IFLforTFC repository. The notebook will automatically download it, but you can also obtain it manually:
  • Direct link:dataset.parquet
  • Contents: 3,163,462 IP flow records with 72 features and application labels for 10 traffic classes
Repository Structure
1. VanillaAE
This Jupyter Notebook contains all resources related to the Vanilla Autoencoder used in the paper. It includes the entire workflow:
  • Data Preprocessing: Includes steps like outlier removal and robust scaling.
  • Autoencoder Architecture: Details of the Vanilla Autoencoder model used for IP flow record compression.
  • Training Process: Training configuration, loss function, optimizer, and training duration.
  • Results Analysis: Contains visualizations and metrics for model performance.
2. VanillaAEvsDAE
This Jupyter Notebook provides a comparative analysis between the Vanilla Autoencoder and the Denoising Autoencoder (DAE).
How to Use
  1. Install dependencies:pip install torch scikit-learn pandas numpy matplotlib seaborn scipy joblib requests
  2. Run VanillaAE.ipynb: Downloads the dataset and reproduces all experiments and results from the paper.
  3. Run VanillaAEvsDAE.ipynb: Comparative analysis between Vanilla AE and Denoising AE. Requires dataset.parquet (produced by step 2).
Both notebooks include pre-computed cell outputs matching the final results reported in the paper.
About
Supporting page for the manuscript titled, "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Topics
autoencodercompressionip-flowmachine-learningnetwork-traffic-classificationsize-reduction
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
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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" + '

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Repository files navigation
AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression
This repository contains all the digital artifacts associated with the paper "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Dataset
The dataset used in this work is available in the IFLforTFC repository. The notebook will automatically download it, but you can also obtain it manually:
  • Direct link:dataset.parquet
  • Contents: 3,163,462 IP flow records with 72 features and application labels for 10 traffic classes
Repository Structure
1. VanillaAE
This Jupyter Notebook contains all resources related to the Vanilla Autoencoder used in the paper. It includes the entire workflow:
  • Data Preprocessing: Includes steps like outlier removal and robust scaling.
  • Autoencoder Architecture: Details of the Vanilla Autoencoder model used for IP flow record compression.
  • Training Process: Training configuration, loss function, optimizer, and training duration.
  • Results Analysis: Contains visualizations and metrics for model performance.
2. VanillaAEvsDAE
This Jupyter Notebook provides a comparative analysis between the Vanilla Autoencoder and the Denoising Autoencoder (DAE).
How to Use
  1. Install dependencies:pip install torch scikit-learn pandas numpy matplotlib seaborn scipy joblib requests
  2. Run VanillaAE.ipynb: Downloads the dataset and reproduces all experiments and results from the paper.
  3. Run VanillaAEvsDAE.ipynb: Comparative analysis between Vanilla AE and Denoising AE. Requires dataset.parquet (produced by step 2).
Both notebooks include pre-computed cell outputs matching the final results reported in the paper.
About
Supporting page for the manuscript titled, "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Topics
autoencodercompressionip-flowmachine-learningnetwork-traffic-classificationsize-reduction
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Contributors
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, '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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AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression
This repository contains all the digital artifacts associated with the paper "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Dataset
The dataset used in this work is available in the IFLforTFC repository. The notebook will automatically download it, but you can also obtain it manually:
  • Direct link:dataset.parquet
  • Contents: 3,163,462 IP flow records with 72 features and application labels for 10 traffic classes
Repository Structure
1. VanillaAE
This Jupyter Notebook contains all resources related to the Vanilla Autoencoder used in the paper. It includes the entire workflow:
  • Data Preprocessing: Includes steps like outlier removal and robust scaling.
  • Autoencoder Architecture: Details of the Vanilla Autoencoder model used for IP flow record compression.
  • Training Process: Training configuration, loss function, optimizer, and training duration.
  • Results Analysis: Contains visualizations and metrics for model performance.
2. VanillaAEvsDAE
This Jupyter Notebook provides a comparative analysis between the Vanilla Autoencoder and the Denoising Autoencoder (DAE).
How to Use
  1. Install dependencies:pip install torch scikit-learn pandas numpy matplotlib seaborn scipy joblib requests
  2. Run VanillaAE.ipynb: Downloads the dataset and reproduces all experiments and results from the paper.
  3. Run VanillaAEvsDAE.ipynb: Comparative analysis between Vanilla AE and Denoising AE. Requires dataset.parquet (produced by step 2).
Both notebooks include pre-computed cell outputs matching the final results reported in the paper.
About
Supporting page for the manuscript titled, "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Topics
autoencodercompressionip-flowmachine-learningnetwork-traffic-classificationsize-reduction
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 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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VanillaAE.ipynb
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Repository files navigation
AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression
This repository contains all the digital artifacts associated with the paper "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Dataset
The dataset used in this work is available in the IFLforTFC repository. The notebook will automatically download it, but you can also obtain it manually:
  • Direct link:dataset.parquet
  • Contents: 3,163,462 IP flow records with 72 features and application labels for 10 traffic classes
Repository Structure
1. VanillaAE
This Jupyter Notebook contains all resources related to the Vanilla Autoencoder used in the paper. It includes the entire workflow:
  • Data Preprocessing: Includes steps like outlier removal and robust scaling.
  • Autoencoder Architecture: Details of the Vanilla Autoencoder model used for IP flow record compression.
  • Training Process: Training configuration, loss function, optimizer, and training duration.
  • Results Analysis: Contains visualizations and metrics for model performance.
2. VanillaAEvsDAE
This Jupyter Notebook provides a comparative analysis between the Vanilla Autoencoder and the Denoising Autoencoder (DAE).
How to Use
  1. Install dependencies:pip install torch scikit-learn pandas numpy matplotlib seaborn scipy joblib requests
  2. Run VanillaAE.ipynb: Downloads the dataset and reproduces all experiments and results from the paper.
  3. Run VanillaAEvsDAE.ipynb: Comparative analysis between Vanilla AE and Denoising AE. Requires dataset.parquet (produced by step 2).
Both notebooks include pre-computed cell outputs matching the final results reported in the paper.
About
Supporting page for the manuscript titled, "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Topics
autoencodercompressionip-flowmachine-learningnetwork-traffic-classificationsize-reduction
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 watching
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0 forks
Report repository
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); }
})();
})();

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AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression
This repository contains all the digital artifacts associated with the paper "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Dataset
The dataset used in this work is available in the IFLforTFC repository. The notebook will automatically download it, but you can also obtain it manually:
  • Direct link:dataset.parquet
  • Contents: 3,163,462 IP flow records with 72 features and application labels for 10 traffic classes
Repository Structure
1. VanillaAE
This Jupyter Notebook contains all resources related to the Vanilla Autoencoder used in the paper. It includes the entire workflow:
  • Data Preprocessing: Includes steps like outlier removal and robust scaling.
  • Autoencoder Architecture: Details of the Vanilla Autoencoder model used for IP flow record compression.
  • Training Process: Training configuration, loss function, optimizer, and training duration.
  • Results Analysis: Contains visualizations and metrics for model performance.
2. VanillaAEvsDAE
This Jupyter Notebook provides a comparative analysis between the Vanilla Autoencoder and the Denoising Autoencoder (DAE).
How to Use
  1. Install dependencies:pip install torch scikit-learn pandas numpy matplotlib seaborn scipy joblib requests
  2. Run VanillaAE.ipynb: Downloads the dataset and reproduces all experiments and results from the paper.
  3. Run VanillaAEvsDAE.ipynb: Comparative analysis between Vanilla AE and Denoising AE. Requires dataset.parquet (produced by step 2).
Both notebooks include pre-computed cell outputs matching the final results reported in the paper.
About
Supporting page for the manuscript titled, "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."
Topics
autoencodercompressionip-flowmachine-learningnetwork-traffic-classificationsize-reduction
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