add categories

README.md

@@ -45,17 +45,6 @@ language:
 - multilingual
 size_categories:
 - 1M<n<10M
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    pattern:
-    - "phishing_features_train.csv"
-    - "phishing_url_train.csv"
-  - split: test
-    pattern:
-    - "phishing_features_val.csv"
-    - "phishing_url_val.csv"
 ---
 
 
@@ -65,6 +54,12 @@ To extract features from a website, simply passed the URL and label to `collect_
 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.
 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.
 
+## Malicious URL Categories
+- Defacement
+- Malware
+- Phishing
+
+## Data Analysis
 Here are two images showing the correlation coefficient and correlation of determination between predictor values and the target value `is_malicious`.
 
 ![Correlation Coefficient](https://i.imgur.com/LLD3pmt.png)
@@ -100,6 +95,7 @@ Let's exmain the correlations one by one and cross out any unreasonable or insig
 |   page_rank_decimal   |        |
 
 
+
 ## Pre-training Ideas
 
 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: