FredZhang7
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README.md
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**I have decided to release the auto-moderation models all at once sometime in July/August, 2023. The datasets for training these models will be avaliable first.**
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**INCORRECT DATA ANALYSIS. IM REDOING THEM**
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The *features* dataset is original, and my feature extraction method is covered in [feature_extraction.py](./feature_extraction.py)
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In the *features* dataset, there're 911,180 websites online at the time of data collection. The plots below show the regression line and correlation coefficients of 22+ features extracted and whether the URL is malicious.
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If we could plot the lifespan of URLs, we could see that the oldest website has been online since Nov 7th, 2008, while the most recent phishing websites appeared as late as July 10th, 2023.
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1. a small multilingual BERT model to output the confidence level of a URL being malicious to model #2, by finetuning on 2,436,727 legitimate and malicious URLs
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2. (probably) LightGBM to analyze the confidence level, along with roughly 19 extracted features
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This way, I can make the most out of the limited phishing websites avaliable.
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![Phish Eater Data Analysis](https://i.imgur.com/Hctxv4h.png)
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## Source of the URLs
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- https://moz.com/top500
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---
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**I have decided to release the auto-moderation models all at once sometime in July/August, 2023. The datasets for training these models will be avaliable first.**
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The *features* dataset is original, and my feature extraction method is covered in [feature_extraction.py](./feature_extraction.py)
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In the *features* dataset, there're 911,180 websites online at the time of data collection. The plots below show the regression line and correlation coefficients of 22+ features extracted and whether the URL is malicious.
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If we could plot the lifespan of URLs, we could see that the oldest website has been online since Nov 7th, 2008, while the most recent phishing websites appeared as late as July 10th, 2023.
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Here are two images showing the correlation coefficient and correlation of determination between predictor values and the target value `is_malicious`.
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![Correlation Coefficient](https://i.imgur.com/LLD3pmt.png)
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![Correlation of Determination](https://i.imgur.com/GJM3Cl6.png)
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For training, I split the classification task into two stages in anticipation of the limited availability of online phishing websites due to their short lifespan, as well as the possibility that research done on phishing is not up-to-date:
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1. a small multilingual BERT model to output the confidence level of a URL being malicious to model #2, by finetuning on 2,436,727 legitimate and malicious URLs
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2. (probably) LightGBM to analyze the confidence level, along with roughly 19 extracted features
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This way, I can make the most out of the limited phishing websites avaliable.
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## Source of the URLs
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- https://moz.com/top500
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