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encrypted-traffic

Here are 21 public repositories matching this topic...

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

  • Updated Apr 5, 2025
  • Jupyter Notebook

CATO: Classification of Application Traffic Online – An AI-powered, privacy-preserving network traffic classifier for multi-UE 5G/6G networks. Built with FastAPI, React, and scikit-learn.

  • Updated Aug 28, 2025
  • JavaScript

MSc dissertation evaluating targeted website fingerprinting on Tor Browser. Built a traffic collection pipeline and feature extraction framework, then trained Random Forest and SVM classifiers on encrypted Tor traffic to identify both visited websites and browser configuration characteristics, achieving up to 88% accuracy

  • Updated Jun 12, 2026
  • Python

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
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;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
encrypted-traffic · GitHub Topics · GitHub
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#

encrypted-traffic

Here are 21 public repositories matching this topic...

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

  • Updated Apr 5, 2025
  • Jupyter Notebook

CATO: Classification of Application Traffic Online – An AI-powered, privacy-preserving network traffic classifier for multi-UE 5G/6G networks. Built with FastAPI, React, and scikit-learn.

  • Updated Aug 28, 2025
  • JavaScript

MSc dissertation evaluating targeted website fingerprinting on Tor Browser. Built a traffic collection pipeline and feature extraction framework, then trained Random Forest and SVM classifiers on encrypted Tor traffic to identify both visited websites and browser configuration characteristics, achieving up to 88% accuracy

  • Updated Jun 12, 2026
  • Python

Add this topic to your repo

To associate your repository with the encrypted-traffic topic, visit your repo's landing page and select "manage topics."

Learn more

, 'i'); if (__m === '*' || __re.test(location.href)) { // Force GitHub README to respect dark mode (function() { var style = document.createElement('style'); style.textContent = ' .markdown-body { color-scheme: dark light; } .markdown-body pre { background: #161b22 !important; } .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; } .markdown-body table th, .markdown-body table td { border-color: #30363d !important; } .markdown-body img { background: #0d1117; } .markdown-body blockquote { border-left-color: #8b949e; } .markdown-body hr { border-color: #30363d; } '; document.head.appendChild(style); })(); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' encrypted-traffic · GitHub Topics · GitHub
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#

encrypted-traffic

Here are 21 public repositories matching this topic...

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

  • Updated Apr 5, 2025
  • Jupyter Notebook

CATO: Classification of Application Traffic Online – An AI-powered, privacy-preserving network traffic classifier for multi-UE 5G/6G networks. Built with FastAPI, React, and scikit-learn.

  • Updated Aug 28, 2025
  • JavaScript

MSc dissertation evaluating targeted website fingerprinting on Tor Browser. Built a traffic collection pipeline and feature extraction framework, then trained Random Forest and SVM classifiers on encrypted Tor traffic to identify both visited websites and browser configuration characteristics, achieving up to 88% accuracy

  • Updated Jun 12, 2026
  • Python

Add this topic to your repo

To associate your repository with the encrypted-traffic topic, visit your repo's landing page and select "manage topics."

Learn more

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

encrypted-traffic

Here are 21 public repositories matching this topic...

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

  • Updated Apr 5, 2025
  • Jupyter Notebook

CATO: Classification of Application Traffic Online – An AI-powered, privacy-preserving network traffic classifier for multi-UE 5G/6G networks. Built with FastAPI, React, and scikit-learn.

  • Updated Aug 28, 2025
  • JavaScript

MSc dissertation evaluating targeted website fingerprinting on Tor Browser. Built a traffic collection pipeline and feature extraction framework, then trained Random Forest and SVM classifiers on encrypted Tor traffic to identify both visited websites and browser configuration characteristics, achieving up to 88% accuracy

  • Updated Jun 12, 2026
  • Python

Add this topic to your repo

To associate your repository with the encrypted-traffic topic, visit your repo's landing page and select "manage topics."

Learn more

, 'i'); if (__m === '*' || __re.test(location.href)) { // Strip utm_, fbclid, gclid, etc. from all links on page (function() { var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content', 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid', 'ref', 'ref_src', 'source', 'medium', 'campaign']; function cleanUrl(url) { try { var u = new URL(url, window.location.origin); var changed = false; trackingParams.forEach(function(p) { if (u.searchParams.has(p)) { u.searchParams.delete(p); changed = true; } }); return changed ? u.toString() : url; } catch (e) { return url; } } function cleanLinks() { document.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } cleanLinks(); var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1) { if (node.tagName === 'A') cleanLinks(); node.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + ' encrypted-traffic · GitHub Topics · GitHub
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#

encrypted-traffic

Here are 21 public repositories matching this topic...

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

  • Updated Apr 5, 2025
  • Jupyter Notebook

CATO: Classification of Application Traffic Online – An AI-powered, privacy-preserving network traffic classifier for multi-UE 5G/6G networks. Built with FastAPI, React, and scikit-learn.

  • Updated Aug 28, 2025
  • JavaScript

MSc dissertation evaluating targeted website fingerprinting on Tor Browser. Built a traffic collection pipeline and feature extraction framework, then trained Random Forest and SVM classifiers on encrypted Tor traffic to identify both visited websites and browser configuration characteristics, achieving up to 88% accuracy

  • Updated Jun 12, 2026
  • Python

Add this topic to your repo

To associate your repository with the encrypted-traffic topic, visit your repo's landing page and select "manage topics."

Learn more

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

encrypted-traffic

Here are 21 public repositories matching this topic...

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

  • Updated Apr 5, 2025
  • Jupyter Notebook

CATO: Classification of Application Traffic Online – An AI-powered, privacy-preserving network traffic classifier for multi-UE 5G/6G networks. Built with FastAPI, React, and scikit-learn.

  • Updated Aug 28, 2025
  • JavaScript

MSc dissertation evaluating targeted website fingerprinting on Tor Browser. Built a traffic collection pipeline and feature extraction framework, then trained Random Forest and SVM classifiers on encrypted Tor traffic to identify both visited websites and browser configuration characteristics, achieving up to 88% accuracy

  • Updated Jun 12, 2026
  • Python

Add this topic to your repo

To associate your repository with the encrypted-traffic topic, visit your repo's landing page and select "manage topics."

Learn more

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

encrypted-traffic

Here are 21 public repositories matching this topic...

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

  • Updated Apr 5, 2025
  • Jupyter Notebook

CATO: Classification of Application Traffic Online – An AI-powered, privacy-preserving network traffic classifier for multi-UE 5G/6G networks. Built with FastAPI, React, and scikit-learn.

  • Updated Aug 28, 2025
  • JavaScript

MSc dissertation evaluating targeted website fingerprinting on Tor Browser. Built a traffic collection pipeline and feature extraction framework, then trained Random Forest and SVM classifiers on encrypted Tor traffic to identify both visited websites and browser configuration characteristics, achieving up to 88% accuracy

  • Updated Jun 12, 2026
  • Python

Add this topic to your repo

To associate your repository with the encrypted-traffic topic, visit your repo's landing page and select "manage topics."

Learn more

, 'i'); if (__m === '*' || __re.test(location.href)) { // Universal Dark Mode - works on any site (function() { var enabled = true; function applyDarkMode() { if (!enabled) return; // Create style element if it doesn't exist var style = document.getElementById('universal-dark-mode-style'); if (!style) { style = document.createElement('style'); style.id = 'universal-dark-mode-style'; document.head.appendChild(style); } // Dark mode CSS - inverts colors but preserves images/video style.textContent = ' /* Invert everything except media */ html { filter: invert(1) hue-rotate(180deg) !important; background: #1a1a2e !important; } /* Restore images, videos, iframes, canvas */ img, video, iframe, canvas, svg, picture, [style*="background-image"] { filter: invert(1) hue-rotate(180deg) !important; } /* Preserve specific elements that should not be inverted */ .no-dark-mode, .no-dark-mode *, [data-theme="light"], [data-theme="light"], .ace_editor, .ace_editor *, .CodeMirror, .CodeMirror *, .monaco-editor, .monaco-editor *, .markdown-body pre, .markdown-body pre *, .highlight, .highlight *, pre code, pre code * { filter: none !important; } /* Fix common UI elements */ .modal, .popup, .dropdown-menu, .tooltip, .popover { filter: invert(1) hue-rotate(180deg) !important; background: #2d2d44 !important; border-color: #444 !important; } /* Scrollbars */ ::-webkit-scrollbar { background: #1a1a2e !important; } ::-webkit-scrollbar-thumb { background: #444 !important; } ::-webkit-scrollbar-thumb:hover { background: #555 !important; } /* Selection */ ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; } ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; } '; } function removeDarkMode() { var style = document.getElementById('universal-dark-mode-style'); if (style) style.remove(); } // Toggle with Alt+Shift+D document.addEventListener('keydown', function(e) { if (e.altKey && e.shiftKey && e.key === 'D') { e.preventDefault(); enabled = !enabled; if (enabled) { applyDarkMode(); console.log('[Universal Dark Mode] Enabled'); } else { removeDarkMode(); console.log('[Universal Dark Mode] Disabled'); } } }); // Apply on load applyDarkMode(); // Re-apply on dynamic content var observer = new MutationObserver(function(mutations) { if (enabled && !document.getElementById('universal-dark-mode-style')) { applyDarkMode(); } }); observer.observe(document.head, { childList: true }); console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle'); })(); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })(); encrypted-traffic · GitHub Topics · GitHub
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#

encrypted-traffic

Here are 21 public repositories matching this topic...

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

  • Updated Apr 5, 2025
  • Jupyter Notebook

CATO: Classification of Application Traffic Online – An AI-powered, privacy-preserving network traffic classifier for multi-UE 5G/6G networks. Built with FastAPI, React, and scikit-learn.

  • Updated Aug 28, 2025
  • JavaScript

MSc dissertation evaluating targeted website fingerprinting on Tor Browser. Built a traffic collection pipeline and feature extraction framework, then trained Random Forest and SVM classifiers on encrypted Tor traffic to identify both visited websites and browser configuration characteristics, achieving up to 88% accuracy

  • Updated Jun 12, 2026
  • Python

Add this topic to your repo

To associate your repository with the encrypted-traffic topic, visit your repo's landing page and select "manage topics."

Learn more