This repository contains a Jupyter Notebook for classifying botnet traffic using machine learning techniques.
The Botnet_Classifier.ipynb
notebook provides an end-to-end solution for identifying and classifying botnet traffic from network data. It includes data preprocessing, feature extraction, model training, and evaluation.
To run the notebook, you'll need to have the following dependencies installed:
-
Python 3.7 or higher
-
Jupyter Notebook
-
pandas
-
numpy
-
matplotlib
You can install the required packages using pip
:
```bash
pip install jupyter pandas numpy scikit-learn matplotlib seaborn
For cloning the repository:
git clone https://github.com/yourusername/Botnet_Classifier.git
- Data Preprocessing: Handles missing values, encodes categorical variables, and scales numerical features.
- Feature Extraction: Extracts meaningful features from the raw data.
- Model Training: Trains various machine learning models such as Decision Trees, Random Forests, and Support Vector Machines.
- Model Evaluation: Evaluates the performance of the models using metrics like accuracy, precision, recall, and F1-score.
- Visualization: Includes visualizations to help understand the data and the model's performance.
def readFile(filename):
"""
Arguments:
filename -- name of the dataset file
Returns:
An RDD containing the data. Each record is a tuple (X, y) where X is an array of features and y is the label.
"""
pass
def normalize(RDD_Xy):
"""
Arguments:
RDD_Xy -- RDD containing data examples. Each record is a tuple (X, y).
Returns:
An RDD rescaled to N(0,1) in each column (mean=0, standard deviation=1).
"""
pass
def train(RDD_Xy, iterations, learning_rate):
"""
Arguments:
RDD_Xy -- RDD containing data examples. Each record is a tuple (X, y).
iterations -- number of iterations of the optimization loop
learning_rate -- learning rate of the gradient descent
Returns:
A list or array containing the weights 'w' and bias 'b' at the end of the training process.
"""
pass
def accuracy(w, b, RDD_Xy):
"""
Arguments:
w -- weights
b -- bias
RDD_Xy -- RDD containing examples to be predicted
Returns:
accuracy -- the number of correct predictions divided by the number of records in RDD_Xy.
"""
pass
def predict(w, b, X):
"""
Arguments:
w -- weights
b -- bias
X -- Example to be predicted
Returns:
Y_pred -- a value (0/1) corresponding to the prediction of X.
"""
pass