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🐟 PhishNet

PhishNet Art

DISCLAIMER: The content provided by PhishNet is exclusively for educational and research purposes ONLY. The training data for our GPT-2 derived model has been carefully cleaned to remove any private or personally identifiable information (PII) to ensure ethical compliance and privacy. The views and opinions expressed are solely those of the authors and do not reflect any associated organizations. No warranty is provided regarding the accuracy or reliability of the information. Usage of PhishNet and its outputs is at your own risk, with no liability for any resultant damages. This project does not endorse illegal activities and should be used responsibly.

TL;DR

PhishNet is a research project utilizing Reinforced Self-Training (ReST) and fine-tuned GPT-2 to create a high-quality synthetic dataset of phishing emails. Trained on various valuable email datasets (see citations), this project aims to dive into the exploration of adversarial AI and expand our understanding of AI safety.

Citations

  • Radford, A., Wu, J., Child, R., et al. (2019). Language Models are Unsupervised Multitask Learners. Link
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
  • Gulcehre, C., Le Paine, T., Srinivasan, S., et al. (2023). Reinforced Self-Training (ReST) for Language Modeling. arXiv preprint arXiv:2308.08998. Link
@misc{gulcehre2023reinforced,
title={Reinforced Self-Training (ReST) for Language Modeling}, author={Caglar Gulcehre and Tom Le Paine and Srivatsan Srinivasan and Ksenia Konyushkova and Lotte Weerts and Abhishek Sharma and Aditya Siddhant and Alex Ahern and Miaosen Wang and Chenjie Gu and Wolfgang Macherey and Arnaud Doucet and Orhan Firat and Nando de Freitas},
year={2023},
eprint={2308.08998},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
  • The Enron Email Dataset. Carnegie Mellon University. Link.
  • The Enron Email Dataset. Kaggle. Link
  • Fraudulent Email Corpus. Kaggle. Link
  • Spam Mails Database. Kaggle. Link
  • Phishing Email Detection. Kaggle. Link
  • Customer Support Ticket Dataset. Kaggle Link
  • Spam or Not Spam Dataset. Kaggle Link

Table Of Content

About

PhishNet is an experimental research project implementing Reinforced Self-Training (ReST) human-aligned with crafted instructions and fine-tuned models to craft a high-quality synthetic dataset of phishing emails.

Resources

Readme
License
Activity
Custom properties

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

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

Skip to content
BranchesTags
Repository files navigation
🐟 PhishNet
PhishNet Art
DISCLAIMER: The content provided by PhishNet is exclusively for educational and research purposes ONLY. The training data for our GPT-2 derived model has been carefully cleaned to remove any private or personally identifiable information (PII) to ensure ethical compliance and privacy. The views and opinions expressed are solely those of the authors and do not reflect any associated organizations. No warranty is provided regarding the accuracy or reliability of the information. Usage of PhishNet and its outputs is at your own risk, with no liability for any resultant damages. This project does not endorse illegal activities and should be used responsibly.
TL;DR
PhishNet is a research project utilizing Reinforced Self-Training (ReST) and fine-tuned GPT-2 to create a high-quality synthetic dataset of phishing emails. Trained on various valuable email datasets (see citations), this project aims to dive into the exploration of adversarial AI and expand our understanding of AI safety.
Citations
  • Radford, A., Wu, J., Child, R., et al. (2019). Language Models are Unsupervised Multitask Learners. Link
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}

  • Gulcehre, C., Le Paine, T., Srinivasan, S., et al. (2023). Reinforced Self-Training (ReST) for Language Modeling. arXiv preprint arXiv:2308.08998. Link
@misc{gulcehre2023reinforced,
title={Reinforced Self-Training (ReST) for Language Modeling}, author={Caglar Gulcehre and Tom Le Paine and Srivatsan Srinivasan and Ksenia Konyushkova and Lotte Weerts and Abhishek Sharma and Aditya Siddhant and Alex Ahern and Miaosen Wang and Chenjie Gu and Wolfgang Macherey and Arnaud Doucet and Orhan Firat and Nando de Freitas},
year={2023},
eprint={2308.08998},
archivePrefix={arXiv},
primaryClass={cs.CL}
}

  • The Enron Email Dataset. Carnegie Mellon University. Link.
  • The Enron Email Dataset. Kaggle. Link
  • Fraudulent Email Corpus. Kaggle. Link
  • Spam Mails Database. Kaggle. Link
  • Phishing Email Detection. Kaggle. Link
  • Customer Support Ticket Dataset. Kaggle Link
  • Spam or Not Spam Dataset. Kaggle Link
Table Of Content
About
PhishNet is an experimental research project implementing Reinforced Self-Training (ReST) human-aligned with crafted instructions and fine-tuned models to craft a high-quality synthetic dataset of phishing emails.
Resources
Readme
License
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 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
🐟 PhishNet
PhishNet Art
DISCLAIMER: The content provided by PhishNet is exclusively for educational and research purposes ONLY. The training data for our GPT-2 derived model has been carefully cleaned to remove any private or personally identifiable information (PII) to ensure ethical compliance and privacy. The views and opinions expressed are solely those of the authors and do not reflect any associated organizations. No warranty is provided regarding the accuracy or reliability of the information. Usage of PhishNet and its outputs is at your own risk, with no liability for any resultant damages. This project does not endorse illegal activities and should be used responsibly.
TL;DR
PhishNet is a research project utilizing Reinforced Self-Training (ReST) and fine-tuned GPT-2 to create a high-quality synthetic dataset of phishing emails. Trained on various valuable email datasets (see citations), this project aims to dive into the exploration of adversarial AI and expand our understanding of AI safety.
Citations
  • Radford, A., Wu, J., Child, R., et al. (2019). Language Models are Unsupervised Multitask Learners. Link
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}

  • Gulcehre, C., Le Paine, T., Srinivasan, S., et al. (2023). Reinforced Self-Training (ReST) for Language Modeling. arXiv preprint arXiv:2308.08998. Link
@misc{gulcehre2023reinforced,
title={Reinforced Self-Training (ReST) for Language Modeling}, author={Caglar Gulcehre and Tom Le Paine and Srivatsan Srinivasan and Ksenia Konyushkova and Lotte Weerts and Abhishek Sharma and Aditya Siddhant and Alex Ahern and Miaosen Wang and Chenjie Gu and Wolfgang Macherey and Arnaud Doucet and Orhan Firat and Nando de Freitas},
year={2023},
eprint={2308.08998},
archivePrefix={arXiv},
primaryClass={cs.CL}
}

  • The Enron Email Dataset. Carnegie Mellon University. Link.
  • The Enron Email Dataset. Kaggle. Link
  • Fraudulent Email Corpus. Kaggle. Link
  • Spam Mails Database. Kaggle. Link
  • Phishing Email Detection. Kaggle. Link
  • Customer Support Ticket Dataset. Kaggle Link
  • Spam or Not Spam Dataset. Kaggle Link
Table Of Content
About
PhishNet is an experimental research project implementing Reinforced Self-Training (ReST) human-aligned with crafted instructions and fine-tuned models to craft a high-quality synthetic dataset of phishing emails.
Resources
Readme
License
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 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
🐟 PhishNet
PhishNet Art
DISCLAIMER: The content provided by PhishNet is exclusively for educational and research purposes ONLY. The training data for our GPT-2 derived model has been carefully cleaned to remove any private or personally identifiable information (PII) to ensure ethical compliance and privacy. The views and opinions expressed are solely those of the authors and do not reflect any associated organizations. No warranty is provided regarding the accuracy or reliability of the information. Usage of PhishNet and its outputs is at your own risk, with no liability for any resultant damages. This project does not endorse illegal activities and should be used responsibly.
TL;DR
PhishNet is a research project utilizing Reinforced Self-Training (ReST) and fine-tuned GPT-2 to create a high-quality synthetic dataset of phishing emails. Trained on various valuable email datasets (see citations), this project aims to dive into the exploration of adversarial AI and expand our understanding of AI safety.
Citations
  • Radford, A., Wu, J., Child, R., et al. (2019). Language Models are Unsupervised Multitask Learners. Link
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}

  • Gulcehre, C., Le Paine, T., Srinivasan, S., et al. (2023). Reinforced Self-Training (ReST) for Language Modeling. arXiv preprint arXiv:2308.08998. Link
@misc{gulcehre2023reinforced,
title={Reinforced Self-Training (ReST) for Language Modeling}, author={Caglar Gulcehre and Tom Le Paine and Srivatsan Srinivasan and Ksenia Konyushkova and Lotte Weerts and Abhishek Sharma and Aditya Siddhant and Alex Ahern and Miaosen Wang and Chenjie Gu and Wolfgang Macherey and Arnaud Doucet and Orhan Firat and Nando de Freitas},
year={2023},
eprint={2308.08998},
archivePrefix={arXiv},
primaryClass={cs.CL}
}

  • The Enron Email Dataset. Carnegie Mellon University. Link.
  • The Enron Email Dataset. Kaggle. Link
  • Fraudulent Email Corpus. Kaggle. Link
  • Spam Mails Database. Kaggle. Link
  • Phishing Email Detection. Kaggle. Link
  • Customer Support Ticket Dataset. Kaggle Link
  • Spam or Not Spam Dataset. Kaggle Link
Table Of Content
About
PhishNet is an experimental research project implementing Reinforced Self-Training (ReST) human-aligned with crafted instructions and fine-tuned models to craft a high-quality synthetic dataset of phishing emails.
Resources
Readme
License
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 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
🐟 PhishNet
PhishNet Art
DISCLAIMER: The content provided by PhishNet is exclusively for educational and research purposes ONLY. The training data for our GPT-2 derived model has been carefully cleaned to remove any private or personally identifiable information (PII) to ensure ethical compliance and privacy. The views and opinions expressed are solely those of the authors and do not reflect any associated organizations. No warranty is provided regarding the accuracy or reliability of the information. Usage of PhishNet and its outputs is at your own risk, with no liability for any resultant damages. This project does not endorse illegal activities and should be used responsibly.
TL;DR
PhishNet is a research project utilizing Reinforced Self-Training (ReST) and fine-tuned GPT-2 to create a high-quality synthetic dataset of phishing emails. Trained on various valuable email datasets (see citations), this project aims to dive into the exploration of adversarial AI and expand our understanding of AI safety.
Citations
  • Radford, A., Wu, J., Child, R., et al. (2019). Language Models are Unsupervised Multitask Learners. Link
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}

  • Gulcehre, C., Le Paine, T., Srinivasan, S., et al. (2023). Reinforced Self-Training (ReST) for Language Modeling. arXiv preprint arXiv:2308.08998. Link
@misc{gulcehre2023reinforced,
title={Reinforced Self-Training (ReST) for Language Modeling}, author={Caglar Gulcehre and Tom Le Paine and Srivatsan Srinivasan and Ksenia Konyushkova and Lotte Weerts and Abhishek Sharma and Aditya Siddhant and Alex Ahern and Miaosen Wang and Chenjie Gu and Wolfgang Macherey and Arnaud Doucet and Orhan Firat and Nando de Freitas},
year={2023},
eprint={2308.08998},
archivePrefix={arXiv},
primaryClass={cs.CL}
}

  • The Enron Email Dataset. Carnegie Mellon University. Link.
  • The Enron Email Dataset. Kaggle. Link
  • Fraudulent Email Corpus. Kaggle. Link
  • Spam Mails Database. Kaggle. Link
  • Phishing Email Detection. Kaggle. Link
  • Customer Support Ticket Dataset. Kaggle Link
  • Spam or Not Spam Dataset. Kaggle Link
Table Of Content
About
PhishNet is an experimental research project implementing Reinforced Self-Training (ReST) human-aligned with crafted instructions and fine-tuned models to craft a high-quality synthetic dataset of phishing emails.
Resources
Readme
License
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 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
🐟 PhishNet
PhishNet Art
DISCLAIMER: The content provided by PhishNet is exclusively for educational and research purposes ONLY. The training data for our GPT-2 derived model has been carefully cleaned to remove any private or personally identifiable information (PII) to ensure ethical compliance and privacy. The views and opinions expressed are solely those of the authors and do not reflect any associated organizations. No warranty is provided regarding the accuracy or reliability of the information. Usage of PhishNet and its outputs is at your own risk, with no liability for any resultant damages. This project does not endorse illegal activities and should be used responsibly.
TL;DR
PhishNet is a research project utilizing Reinforced Self-Training (ReST) and fine-tuned GPT-2 to create a high-quality synthetic dataset of phishing emails. Trained on various valuable email datasets (see citations), this project aims to dive into the exploration of adversarial AI and expand our understanding of AI safety.
Citations
  • Radford, A., Wu, J., Child, R., et al. (2019). Language Models are Unsupervised Multitask Learners. Link
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}

  • Gulcehre, C., Le Paine, T., Srinivasan, S., et al. (2023). Reinforced Self-Training (ReST) for Language Modeling. arXiv preprint arXiv:2308.08998. Link
@misc{gulcehre2023reinforced,
title={Reinforced Self-Training (ReST) for Language Modeling}, author={Caglar Gulcehre and Tom Le Paine and Srivatsan Srinivasan and Ksenia Konyushkova and Lotte Weerts and Abhishek Sharma and Aditya Siddhant and Alex Ahern and Miaosen Wang and Chenjie Gu and Wolfgang Macherey and Arnaud Doucet and Orhan Firat and Nando de Freitas},
year={2023},
eprint={2308.08998},
archivePrefix={arXiv},
primaryClass={cs.CL}
}

  • The Enron Email Dataset. Carnegie Mellon University. Link.
  • The Enron Email Dataset. Kaggle. Link
  • Fraudulent Email Corpus. Kaggle. Link
  • Spam Mails Database. Kaggle. Link
  • Phishing Email Detection. Kaggle. Link
  • Customer Support Ticket Dataset. Kaggle Link
  • Spam or Not Spam Dataset. Kaggle Link
Table Of Content
About
PhishNet is an experimental research project implementing Reinforced Self-Training (ReST) human-aligned with crafted instructions and fine-tuned models to craft a high-quality synthetic dataset of phishing emails.
Resources
Readme
License
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 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");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
🐟 PhishNet
PhishNet Art
DISCLAIMER: The content provided by PhishNet is exclusively for educational and research purposes ONLY. The training data for our GPT-2 derived model has been carefully cleaned to remove any private or personally identifiable information (PII) to ensure ethical compliance and privacy. The views and opinions expressed are solely those of the authors and do not reflect any associated organizations. No warranty is provided regarding the accuracy or reliability of the information. Usage of PhishNet and its outputs is at your own risk, with no liability for any resultant damages. This project does not endorse illegal activities and should be used responsibly.
TL;DR
PhishNet is a research project utilizing Reinforced Self-Training (ReST) and fine-tuned GPT-2 to create a high-quality synthetic dataset of phishing emails. Trained on various valuable email datasets (see citations), this project aims to dive into the exploration of adversarial AI and expand our understanding of AI safety.
Citations
  • Radford, A., Wu, J., Child, R., et al. (2019). Language Models are Unsupervised Multitask Learners. Link
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}

  • Gulcehre, C., Le Paine, T., Srinivasan, S., et al. (2023). Reinforced Self-Training (ReST) for Language Modeling. arXiv preprint arXiv:2308.08998. Link
@misc{gulcehre2023reinforced,
title={Reinforced Self-Training (ReST) for Language Modeling}, author={Caglar Gulcehre and Tom Le Paine and Srivatsan Srinivasan and Ksenia Konyushkova and Lotte Weerts and Abhishek Sharma and Aditya Siddhant and Alex Ahern and Miaosen Wang and Chenjie Gu and Wolfgang Macherey and Arnaud Doucet and Orhan Firat and Nando de Freitas},
year={2023},
eprint={2308.08998},
archivePrefix={arXiv},
primaryClass={cs.CL}
}

  • The Enron Email Dataset. Carnegie Mellon University. Link.
  • The Enron Email Dataset. Kaggle. Link
  • Fraudulent Email Corpus. Kaggle. Link
  • Spam Mails Database. Kaggle. Link
  • Phishing Email Detection. Kaggle. Link
  • Customer Support Ticket Dataset. Kaggle Link
  • Spam or Not Spam Dataset. Kaggle Link
Table Of Content
About
PhishNet is an experimental research project implementing Reinforced Self-Training (ReST) human-aligned with crafted instructions and fine-tuned models to craft a high-quality synthetic dataset of phishing emails.
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🐟 PhishNet
PhishNet Art
DISCLAIMER: The content provided by PhishNet is exclusively for educational and research purposes ONLY. The training data for our GPT-2 derived model has been carefully cleaned to remove any private or personally identifiable information (PII) to ensure ethical compliance and privacy. The views and opinions expressed are solely those of the authors and do not reflect any associated organizations. No warranty is provided regarding the accuracy or reliability of the information. Usage of PhishNet and its outputs is at your own risk, with no liability for any resultant damages. This project does not endorse illegal activities and should be used responsibly.
TL;DR
PhishNet is a research project utilizing Reinforced Self-Training (ReST) and fine-tuned GPT-2 to create a high-quality synthetic dataset of phishing emails. Trained on various valuable email datasets (see citations), this project aims to dive into the exploration of adversarial AI and expand our understanding of AI safety.
Citations
  • Radford, A., Wu, J., Child, R., et al. (2019). Language Models are Unsupervised Multitask Learners. Link
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}

  • Gulcehre, C., Le Paine, T., Srinivasan, S., et al. (2023). Reinforced Self-Training (ReST) for Language Modeling. arXiv preprint arXiv:2308.08998. Link
@misc{gulcehre2023reinforced,
title={Reinforced Self-Training (ReST) for Language Modeling}, author={Caglar Gulcehre and Tom Le Paine and Srivatsan Srinivasan and Ksenia Konyushkova and Lotte Weerts and Abhishek Sharma and Aditya Siddhant and Alex Ahern and Miaosen Wang and Chenjie Gu and Wolfgang Macherey and Arnaud Doucet and Orhan Firat and Nando de Freitas},
year={2023},
eprint={2308.08998},
archivePrefix={arXiv},
primaryClass={cs.CL}
}

  • The Enron Email Dataset. Carnegie Mellon University. Link.
  • The Enron Email Dataset. Kaggle. Link
  • Fraudulent Email Corpus. Kaggle. Link
  • Spam Mails Database. Kaggle. Link
  • Phishing Email Detection. Kaggle. Link
  • Customer Support Ticket Dataset. Kaggle Link
  • Spam or Not Spam Dataset. Kaggle Link
Table Of Content
About
PhishNet is an experimental research project implementing Reinforced Self-Training (ReST) human-aligned with crafted instructions and fine-tuned models to craft a high-quality synthetic dataset of phishing emails.
Resources
Readme
License
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages