kimiyashabani / spark_botnet_classifier

Botnet Classifier

This repository contains a Jupyter Notebook for classifying botnet traffic using machine learning techniques.

Overview

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.

Table of Contents

• Installation
• Usage
• Function

Installation

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

Usage

• 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.

Function Definitions

readFile

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