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Traffic Classification & Prioritization

The overall goal of this project is to improve the quality of multimedia streaming. This becomes important when other users/applications are downloading large files etc., and it leads to the multimedia quality deterioration. So we first identify the multimedia flow (classification) and then prioritize it. Current Version is 1.0, and it is released on June 22, 2016.

Outline

Traffic classification is useful for traffic engineering and network security. Network administrators can use it to allocate, control and manage the network resources as per their requirements. Classification methods can be used to classify P2P traffic, encrypted traffic, web, streaming, download or any specific application.

Our classification model classifies traffic into two classes, i.e., multimedia and download. We used supervised machine learning algorithms (Decision Tree and K-NN) to build the classification model. This model is trained using pre-labeled training instances and later used to classify the traffic in real-time. We use packet level statistics (average packet size, average inter-arrival time, receiver's window size, flow duration etc.) as features for classification algorithms.

Prioritization module ensures that once the flow is identified as multimedia it will get higher priority over the download flows. We used HTB (Hierarchical Token Bucket Filter) for this purpose.

We have also developed heuristics that can automatically label the training data set with some manual inputs, i.e. labeling each flow in the data set as either multimedia or download. These heuristics look at URI of HTTP GET request and search for multimedia file formats in it, if found then it labels that flow as multimedia.

This project can be used to create a large training data, train the classifier and further classify the traffic. Someone may try to add few new features and change specific settings to analyze the classification behavior.

List of modules developed

  • Classification module (2 approaches, K-NN and decision tree)
  • Prioritization module (HTB)
  • Auto-labeling heuristics to create large training data
  • Configure laptop as AP
  • Configure the DHCP server

Directory Structure

  • doc: Contains project documentation
  • scripts: Contains necessary scripts for the setup, classification and prioritization.

Contents

  • A Detailed Report containing an explanation our work in detail.
  • Scripts for various setups, classification and prioritization.
  • A user guide containing the setup and installation instructions.
  • A developer guide which explains the structure of the scripts.

Authors

  • Hiren Patel, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Vidya Sagar Kushwaha, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Prof. Mythili Vutukuru, Dept. of Computer Science and Engineering, IIT Bombay.

Contact Us

  • Hiren Patel, hiren131292[AT]gmail.com
  • Vidya Sagar Kushwaha, vskushwaha21[AT]gmail.com
  • Prof. Mythili Vutukuru, mythili[AT]cse.iitb.ac.in

About

Identification and prioritization of multimedia traffic in wireless access points

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27 stars

Watchers

3 watching

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11 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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Traffic Classification & Prioritization
The overall goal of this project is to improve the quality of multimedia streaming. This becomes important when other users/applications are downloading large files etc., and it leads to the multimedia quality deterioration. So we first identify the multimedia flow (classification) and then prioritize it. Current Version is 1.0, and it is released on June 22, 2016.
Outline
Traffic classification is useful for traffic engineering and network security. Network administrators can use it to allocate, control and manage the network resources as per their requirements. Classification methods can be used to classify P2P traffic, encrypted traffic, web, streaming, download or any specific application.
Our classification model classifies traffic into two classes, i.e., multimedia and download. We used supervised machine learning algorithms (Decision Tree and K-NN) to build the classification model. This model is trained using pre-labeled training instances and later used to classify the traffic in real-time. We use packet level statistics (average packet size, average inter-arrival time, receiver's window size, flow duration etc.) as features for classification algorithms.
Prioritization module ensures that once the flow is identified as multimedia it will get higher priority over the download flows. We used HTB (Hierarchical Token Bucket Filter) for this purpose.
We have also developed heuristics that can automatically label the training data set with some manual inputs, i.e. labeling each flow in the data set as either multimedia or download. These heuristics look at URI of HTTP GET request and search for multimedia file formats in it, if found then it labels that flow as multimedia.
This project can be used to create a large training data, train the classifier and further classify the traffic. Someone may try to add few new features and change specific settings to analyze the classification behavior.
List of modules developed
  • Classification module (2 approaches, K-NN and decision tree)
  • Prioritization module (HTB)
  • Auto-labeling heuristics to create large training data
  • Configure laptop as AP
  • Configure the DHCP server
Directory Structure
  • doc: Contains project documentation
  • scripts: Contains necessary scripts for the setup, classification and prioritization.
Contents
  • A Detailed Report containing an explanation our work in detail.
  • Scripts for various setups, classification and prioritization.
  • A user guide containing the setup and installation instructions.
  • A developer guide which explains the structure of the scripts.
Authors
  • Hiren Patel, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Vidya Sagar Kushwaha, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Prof. Mythili Vutukuru, Dept. of Computer Science and Engineering, IIT Bombay.
Contact Us
  • Hiren Patel, hiren131292[AT]gmail.com
  • Vidya Sagar Kushwaha, vskushwaha21[AT]gmail.com
  • Prof. Mythili Vutukuru, mythili[AT]cse.iitb.ac.in
About
Identification and prioritization of multimedia traffic in wireless access points
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3 watching
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11 forks
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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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Traffic Classification & Prioritization
The overall goal of this project is to improve the quality of multimedia streaming. This becomes important when other users/applications are downloading large files etc., and it leads to the multimedia quality deterioration. So we first identify the multimedia flow (classification) and then prioritize it. Current Version is 1.0, and it is released on June 22, 2016.
Outline
Traffic classification is useful for traffic engineering and network security. Network administrators can use it to allocate, control and manage the network resources as per their requirements. Classification methods can be used to classify P2P traffic, encrypted traffic, web, streaming, download or any specific application.
Our classification model classifies traffic into two classes, i.e., multimedia and download. We used supervised machine learning algorithms (Decision Tree and K-NN) to build the classification model. This model is trained using pre-labeled training instances and later used to classify the traffic in real-time. We use packet level statistics (average packet size, average inter-arrival time, receiver's window size, flow duration etc.) as features for classification algorithms.
Prioritization module ensures that once the flow is identified as multimedia it will get higher priority over the download flows. We used HTB (Hierarchical Token Bucket Filter) for this purpose.
We have also developed heuristics that can automatically label the training data set with some manual inputs, i.e. labeling each flow in the data set as either multimedia or download. These heuristics look at URI of HTTP GET request and search for multimedia file formats in it, if found then it labels that flow as multimedia.
This project can be used to create a large training data, train the classifier and further classify the traffic. Someone may try to add few new features and change specific settings to analyze the classification behavior.
List of modules developed
  • Classification module (2 approaches, K-NN and decision tree)
  • Prioritization module (HTB)
  • Auto-labeling heuristics to create large training data
  • Configure laptop as AP
  • Configure the DHCP server
Directory Structure
  • doc: Contains project documentation
  • scripts: Contains necessary scripts for the setup, classification and prioritization.
Contents
  • A Detailed Report containing an explanation our work in detail.
  • Scripts for various setups, classification and prioritization.
  • A user guide containing the setup and installation instructions.
  • A developer guide which explains the structure of the scripts.
Authors
  • Hiren Patel, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Vidya Sagar Kushwaha, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Prof. Mythili Vutukuru, Dept. of Computer Science and Engineering, IIT Bombay.
Contact Us
  • Hiren Patel, hiren131292[AT]gmail.com
  • Vidya Sagar Kushwaha, vskushwaha21[AT]gmail.com
  • Prof. Mythili Vutukuru, mythili[AT]cse.iitb.ac.in
About
Identification and prioritization of multimedia traffic in wireless access points
Resources
Readme
Activity
Custom properties
Stars
27 stars
Watchers
3 watching
Forks
11 forks
Report repository
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, '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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Traffic Classification & Prioritization
The overall goal of this project is to improve the quality of multimedia streaming. This becomes important when other users/applications are downloading large files etc., and it leads to the multimedia quality deterioration. So we first identify the multimedia flow (classification) and then prioritize it. Current Version is 1.0, and it is released on June 22, 2016.
Outline
Traffic classification is useful for traffic engineering and network security. Network administrators can use it to allocate, control and manage the network resources as per their requirements. Classification methods can be used to classify P2P traffic, encrypted traffic, web, streaming, download or any specific application.
Our classification model classifies traffic into two classes, i.e., multimedia and download. We used supervised machine learning algorithms (Decision Tree and K-NN) to build the classification model. This model is trained using pre-labeled training instances and later used to classify the traffic in real-time. We use packet level statistics (average packet size, average inter-arrival time, receiver's window size, flow duration etc.) as features for classification algorithms.
Prioritization module ensures that once the flow is identified as multimedia it will get higher priority over the download flows. We used HTB (Hierarchical Token Bucket Filter) for this purpose.
We have also developed heuristics that can automatically label the training data set with some manual inputs, i.e. labeling each flow in the data set as either multimedia or download. These heuristics look at URI of HTTP GET request and search for multimedia file formats in it, if found then it labels that flow as multimedia.
This project can be used to create a large training data, train the classifier and further classify the traffic. Someone may try to add few new features and change specific settings to analyze the classification behavior.
List of modules developed
  • Classification module (2 approaches, K-NN and decision tree)
  • Prioritization module (HTB)
  • Auto-labeling heuristics to create large training data
  • Configure laptop as AP
  • Configure the DHCP server
Directory Structure
  • doc: Contains project documentation
  • scripts: Contains necessary scripts for the setup, classification and prioritization.
Contents
  • A Detailed Report containing an explanation our work in detail.
  • Scripts for various setups, classification and prioritization.
  • A user guide containing the setup and installation instructions.
  • A developer guide which explains the structure of the scripts.
Authors
  • Hiren Patel, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Vidya Sagar Kushwaha, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Prof. Mythili Vutukuru, Dept. of Computer Science and Engineering, IIT Bombay.
Contact Us
  • Hiren Patel, hiren131292[AT]gmail.com
  • Vidya Sagar Kushwaha, vskushwaha21[AT]gmail.com
  • Prof. Mythili Vutukuru, mythili[AT]cse.iitb.ac.in
About
Identification and prioritization of multimedia traffic in wireless access points
Resources
Readme
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27 stars
Watchers
3 watching
Forks
11 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Traffic Classification & Prioritization
The overall goal of this project is to improve the quality of multimedia streaming. This becomes important when other users/applications are downloading large files etc., and it leads to the multimedia quality deterioration. So we first identify the multimedia flow (classification) and then prioritize it. Current Version is 1.0, and it is released on June 22, 2016.
Outline
Traffic classification is useful for traffic engineering and network security. Network administrators can use it to allocate, control and manage the network resources as per their requirements. Classification methods can be used to classify P2P traffic, encrypted traffic, web, streaming, download or any specific application.
Our classification model classifies traffic into two classes, i.e., multimedia and download. We used supervised machine learning algorithms (Decision Tree and K-NN) to build the classification model. This model is trained using pre-labeled training instances and later used to classify the traffic in real-time. We use packet level statistics (average packet size, average inter-arrival time, receiver's window size, flow duration etc.) as features for classification algorithms.
Prioritization module ensures that once the flow is identified as multimedia it will get higher priority over the download flows. We used HTB (Hierarchical Token Bucket Filter) for this purpose.
We have also developed heuristics that can automatically label the training data set with some manual inputs, i.e. labeling each flow in the data set as either multimedia or download. These heuristics look at URI of HTTP GET request and search for multimedia file formats in it, if found then it labels that flow as multimedia.
This project can be used to create a large training data, train the classifier and further classify the traffic. Someone may try to add few new features and change specific settings to analyze the classification behavior.
List of modules developed
  • Classification module (2 approaches, K-NN and decision tree)
  • Prioritization module (HTB)
  • Auto-labeling heuristics to create large training data
  • Configure laptop as AP
  • Configure the DHCP server
Directory Structure
  • doc: Contains project documentation
  • scripts: Contains necessary scripts for the setup, classification and prioritization.
Contents
  • A Detailed Report containing an explanation our work in detail.
  • Scripts for various setups, classification and prioritization.
  • A user guide containing the setup and installation instructions.
  • A developer guide which explains the structure of the scripts.
Authors
  • Hiren Patel, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Vidya Sagar Kushwaha, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Prof. Mythili Vutukuru, Dept. of Computer Science and Engineering, IIT Bombay.
Contact Us
  • Hiren Patel, hiren131292[AT]gmail.com
  • Vidya Sagar Kushwaha, vskushwaha21[AT]gmail.com
  • Prof. Mythili Vutukuru, mythili[AT]cse.iitb.ac.in
About
Identification and prioritization of multimedia traffic in wireless access points
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Readme
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Stars
27 stars
Watchers
3 watching
Forks
11 forks
Report repository
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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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Traffic Classification & Prioritization
The overall goal of this project is to improve the quality of multimedia streaming. This becomes important when other users/applications are downloading large files etc., and it leads to the multimedia quality deterioration. So we first identify the multimedia flow (classification) and then prioritize it. Current Version is 1.0, and it is released on June 22, 2016.
Outline
Traffic classification is useful for traffic engineering and network security. Network administrators can use it to allocate, control and manage the network resources as per their requirements. Classification methods can be used to classify P2P traffic, encrypted traffic, web, streaming, download or any specific application.
Our classification model classifies traffic into two classes, i.e., multimedia and download. We used supervised machine learning algorithms (Decision Tree and K-NN) to build the classification model. This model is trained using pre-labeled training instances and later used to classify the traffic in real-time. We use packet level statistics (average packet size, average inter-arrival time, receiver's window size, flow duration etc.) as features for classification algorithms.
Prioritization module ensures that once the flow is identified as multimedia it will get higher priority over the download flows. We used HTB (Hierarchical Token Bucket Filter) for this purpose.
We have also developed heuristics that can automatically label the training data set with some manual inputs, i.e. labeling each flow in the data set as either multimedia or download. These heuristics look at URI of HTTP GET request and search for multimedia file formats in it, if found then it labels that flow as multimedia.
This project can be used to create a large training data, train the classifier and further classify the traffic. Someone may try to add few new features and change specific settings to analyze the classification behavior.
List of modules developed
  • Classification module (2 approaches, K-NN and decision tree)
  • Prioritization module (HTB)
  • Auto-labeling heuristics to create large training data
  • Configure laptop as AP
  • Configure the DHCP server
Directory Structure
  • doc: Contains project documentation
  • scripts: Contains necessary scripts for the setup, classification and prioritization.
Contents
  • A Detailed Report containing an explanation our work in detail.
  • Scripts for various setups, classification and prioritization.
  • A user guide containing the setup and installation instructions.
  • A developer guide which explains the structure of the scripts.
Authors
  • Hiren Patel, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Vidya Sagar Kushwaha, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Prof. Mythili Vutukuru, Dept. of Computer Science and Engineering, IIT Bombay.
Contact Us
  • Hiren Patel, hiren131292[AT]gmail.com
  • Vidya Sagar Kushwaha, vskushwaha21[AT]gmail.com
  • Prof. Mythili Vutukuru, mythili[AT]cse.iitb.ac.in
About
Identification and prioritization of multimedia traffic in wireless access points
Resources
Readme
Activity
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Stars
27 stars
Watchers
3 watching
Forks
11 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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Traffic Classification & Prioritization
The overall goal of this project is to improve the quality of multimedia streaming. This becomes important when other users/applications are downloading large files etc., and it leads to the multimedia quality deterioration. So we first identify the multimedia flow (classification) and then prioritize it. Current Version is 1.0, and it is released on June 22, 2016.
Outline
Traffic classification is useful for traffic engineering and network security. Network administrators can use it to allocate, control and manage the network resources as per their requirements. Classification methods can be used to classify P2P traffic, encrypted traffic, web, streaming, download or any specific application.
Our classification model classifies traffic into two classes, i.e., multimedia and download. We used supervised machine learning algorithms (Decision Tree and K-NN) to build the classification model. This model is trained using pre-labeled training instances and later used to classify the traffic in real-time. We use packet level statistics (average packet size, average inter-arrival time, receiver's window size, flow duration etc.) as features for classification algorithms.
Prioritization module ensures that once the flow is identified as multimedia it will get higher priority over the download flows. We used HTB (Hierarchical Token Bucket Filter) for this purpose.
We have also developed heuristics that can automatically label the training data set with some manual inputs, i.e. labeling each flow in the data set as either multimedia or download. These heuristics look at URI of HTTP GET request and search for multimedia file formats in it, if found then it labels that flow as multimedia.
This project can be used to create a large training data, train the classifier and further classify the traffic. Someone may try to add few new features and change specific settings to analyze the classification behavior.
List of modules developed
  • Classification module (2 approaches, K-NN and decision tree)
  • Prioritization module (HTB)
  • Auto-labeling heuristics to create large training data
  • Configure laptop as AP
  • Configure the DHCP server
Directory Structure
  • doc: Contains project documentation
  • scripts: Contains necessary scripts for the setup, classification and prioritization.
Contents
  • A Detailed Report containing an explanation our work in detail.
  • Scripts for various setups, classification and prioritization.
  • A user guide containing the setup and installation instructions.
  • A developer guide which explains the structure of the scripts.
Authors
  • Hiren Patel, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Vidya Sagar Kushwaha, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Prof. Mythili Vutukuru, Dept. of Computer Science and Engineering, IIT Bombay.
Contact Us
  • Hiren Patel, hiren131292[AT]gmail.com
  • Vidya Sagar Kushwaha, vskushwaha21[AT]gmail.com
  • Prof. Mythili Vutukuru, mythili[AT]cse.iitb.ac.in
About
Identification and prioritization of multimedia traffic in wireless access points
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27 stars
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3 watching
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11 forks
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, '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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Repository files navigation
Traffic Classification & Prioritization
The overall goal of this project is to improve the quality of multimedia streaming. This becomes important when other users/applications are downloading large files etc., and it leads to the multimedia quality deterioration. So we first identify the multimedia flow (classification) and then prioritize it. Current Version is 1.0, and it is released on June 22, 2016.
Outline
Traffic classification is useful for traffic engineering and network security. Network administrators can use it to allocate, control and manage the network resources as per their requirements. Classification methods can be used to classify P2P traffic, encrypted traffic, web, streaming, download or any specific application.
Our classification model classifies traffic into two classes, i.e., multimedia and download. We used supervised machine learning algorithms (Decision Tree and K-NN) to build the classification model. This model is trained using pre-labeled training instances and later used to classify the traffic in real-time. We use packet level statistics (average packet size, average inter-arrival time, receiver's window size, flow duration etc.) as features for classification algorithms.
Prioritization module ensures that once the flow is identified as multimedia it will get higher priority over the download flows. We used HTB (Hierarchical Token Bucket Filter) for this purpose.
We have also developed heuristics that can automatically label the training data set with some manual inputs, i.e. labeling each flow in the data set as either multimedia or download. These heuristics look at URI of HTTP GET request and search for multimedia file formats in it, if found then it labels that flow as multimedia.
This project can be used to create a large training data, train the classifier and further classify the traffic. Someone may try to add few new features and change specific settings to analyze the classification behavior.
List of modules developed
  • Classification module (2 approaches, K-NN and decision tree)
  • Prioritization module (HTB)
  • Auto-labeling heuristics to create large training data
  • Configure laptop as AP
  • Configure the DHCP server
Directory Structure
  • doc: Contains project documentation
  • scripts: Contains necessary scripts for the setup, classification and prioritization.
Contents
  • A Detailed Report containing an explanation our work in detail.
  • Scripts for various setups, classification and prioritization.
  • A user guide containing the setup and installation instructions.
  • A developer guide which explains the structure of the scripts.
Authors
  • Hiren Patel, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Vidya Sagar Kushwaha, Master's student (2014-2016), Dept. of Computer Science and Engineering, IIT Bombay.
  • Prof. Mythili Vutukuru, Dept. of Computer Science and Engineering, IIT Bombay.
Contact Us
  • Hiren Patel, hiren131292[AT]gmail.com
  • Vidya Sagar Kushwaha, vskushwaha21[AT]gmail.com
  • Prof. Mythili Vutukuru, mythili[AT]cse.iitb.ac.in
About
Identification and prioritization of multimedia traffic in wireless access points
Resources
Readme
Activity
Custom properties
Stars
27 stars
Watchers
3 watching
Forks
11 forks
Report repository
Releases
Packages
Contributors
Languages