Datasets:

FredZhang7
/

malicious-website-features-2.4M

Tasks:

Text Classification

Feature Extraction

Tabular Classification

Languages:

Size:

License:

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FredZhang7 commited on Jul 20, 2023

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@@ -100,7 +100,7 @@ Let's exmain the correlations one by one and cross out any unreasonable or insig
 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:
 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
-2. (probably) LightGBM to analyze the confidence level, along with roughly 19 extracted features
 This way, I can make the most out of the limited phishing websites avaliable.

 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:
 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
+2. (probably) LightGBM to analyze the confidence level, along with roughly 10 extracted features
 This way, I can make the most out of the limited phishing websites avaliable.