ML-Driven-Cyber-Anomaly-Detection-

This project focuses on applying machine learning techniques to detect cyber anomalies using the UGRansom dataset. It demonstrates the implementation and comparison of various classification algorithms for identifying different types of network intrusions and ransomware attacks.

Key Features:

• Utilizes the UGRansom dataset for analyzing ransomware and zero-day cyberattacks
• Implements and compares multiple ML classifiers:
  • Artificial Neural Network (ANN)
  • Naive Bayes
  • Support Vector Machine (SVM)
  • Random Forest
  • Gradient Boosting
  • Bagging Classifier
• Performs extensive Exploratory Data Analysis (EDA)
• Includes data preprocessing and feature engineering
• Provides detailed performance metrics and visualizations

Technologies Used:

• Python
• Pandas, NumPy
• Scikit-learn
• TensorFlow/Keras
• Seaborn, Matplotlib

How to Use This Repository:

1. Clone the repository:

   git clone https://github.com/Saurav0129/ML-Driven-Cyber-Anomaly-Detection.git
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Open and run the Jupyter notebook:

   jupyter notebook "ML-Driven Cyber Anomaly Detection.ipynb"
   

4. Follow the notebook to understand the analysis and model implementations.