README.md

metadata

title: Cyberattack Detection ML Approach
emoji: 🛡️
colorFrom: red
colorTo: pink
sdk: streamlit
sdk_version: 1.42.2
app_file: app.py
pinned: false
license: mit
short_description: UNSW Dataset in an ML-based analysis

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Cyber Attack Detection ML Approach

This Streamlit app provides an interactive analysis of the UNSW-NB15 dataset, a popular benchmark for evaluating network intrusion detection systems. The app leverages machine learning techniques to classify network traffic as either normal or indicative of various attack types.

About the UNSW-NB15 Dataset

The UNSW-NB15 dataset was created by the Cyber Security Lab at the University of New South Wales, Canberra. It's a comprehensive dataset containing network traffic captures (tcpdump) and system call traces. The dataset includes a variety of modern attack types, making it a valuable resource for training and testing intrusion detection systems. Key features of the dataset include:

• Diverse Attack Types: Covers a wide range of attacks such as Fuzzers, Backdoor, DoS, Exploits, Generic, Reconnaissance, Shellcode, and Worms.
• Realistic Network Traffic: Generated using a realistic network environment, simulating real-world scenarios.
• Labeled Data: Each network flow is labeled with its corresponding attack type or as normal traffic, enabling supervised learning.

App Purpose

This app aims to:

1. Visualize and Explore the Data: Provide an interface to view the dataset's structure, data types, and descriptive statistics. This allows users to understand the characteristics of the UNSW-NB15 dataset.
2. Train and Evaluate Machine Learning Models: Implement and compare the performance of several machine learning classifiers, specifically:
   • Naive Bayes
   • Decision Tree
   • K-Nearest Neighbors
3. Analyze Model Performance: Present confusion matrices and classification reports to evaluate the effectiveness of each model in detecting different attack types. This helps users understand the strengths and weaknesses of each algorithm.
4. Facilitate Learning: Serve as an educational tool for learning about network intrusion detection, machine learning classification, and dataset analysis.

Installation

To run this app, you need to have Python installed along with the following libraries:

• streamlit
• datasets
• pandas
• huggingface_hub
• scikit-learn
• seaborn
• matplotlib
• numpy
• Pillow

You can install the required libraries using pip:

pip install streamlit datasets pandas huggingface_hub scikit-learn seaborn matplotlib numpy Pillow

Usage

1. Ensure you have set the HF_TOKEN environment variable with your Hugging Face token.
2. Run the Streamlit app:

streamlit run app.py

Features

• Dataset Information: View the dataset's structure, data types, and descriptive statistics.
• Naive Bayes Classifier: Train and evaluate a Naive Bayes model.
• Decision Tree Classifier: Train and evaluate a Decision Tree model.
• K-Nearest Neighbor Classifier: Train and evaluate a K-Nearest Neighbor model.
• Confusion Matrix and Classification Report: Visualize the performance of each model.

Screenshots

!Cybersecurity

License

This project is licensed under the MIT License.

Acknowledgements

• The UNSW-NB15 dataset creators at the University of New South Wales, Canberra.
• The Hugging Face team for providing the datasets and tools.