Initial ML Model for TANNER #437
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The test_data.csv file contains the dataset used to evaluate the performance of the machine learning model. It includes a variety of URLs and payloads, pre-processed using TF-IDF (Term Frequency-Inverse Document Frequency) to extract relevant features. The model uses this data to predict the type of web attacks, such as SQL Injection, Cross-Site Scripting (XSS), and Local/Remote File Inclusion (LFI/RFI), and compare its predictions against the actual labels to assess accuracy and other performance metrics.
The train_data.csv file contains the dataset used to train the machine learning model. It includes a variety of URLs and payloads, pre-processed using TF-IDF (Term Frequency-Inverse Document Frequency) to extract relevant features. This data teaches the model to recognize and categorize different types of web attacks, such as SQL Injection, Cross-Site Scripting (XSS), and Local/Remote File Inclusion (LFI/RFI). By learning from this data, the model becomes capable of making accurate predictions on new, unseen data.
This Jupyter Notebook outlines the complete machine learning pipeline for detecting web attacks using a Random Forest Classifier. It includes data loading, cleaning (removal of duplicates, handling missing values, and outlier removal), feature engineering (Label Encoding and TF-IDF transformation), model training, and evaluation. The pipeline also provides insights into class distribution, summary statistics, and feature correlations, all aimed at improving the accuracy and effectiveness of web attack detection in TANNER.
Update README.md
Modified notebook to import libraries, unzip dataset
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Hello, It seems this project is about creating a ML based classifier for TANNER, I am interested in contributing to this project. |
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