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--- |
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title: Cyberattack Detection ML Approach |
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emoji: 🛡️ |
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colorFrom: red |
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colorTo: pink |
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sdk: streamlit |
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sdk_version: 1.42.2 |
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app_file: app.py |
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pinned: false |
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license: mit |
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short_description: UNSW Dataset in an ML-based analysis |
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--- |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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# Cyber Attack Detection ML Approach |
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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. |
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## About the UNSW-NB15 Dataset |
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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: |
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- **Diverse Attack Types:** Covers a wide range of attacks such as Fuzzers, Backdoor, DoS, Exploits, Generic, Reconnaissance, Shellcode, and Worms. |
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- **Realistic Network Traffic:** Generated using a realistic network environment, simulating real-world scenarios. |
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- **Labeled Data:** Each network flow is labeled with its corresponding attack type or as normal traffic, enabling supervised learning. |
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## App Purpose |
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This app aims to: |
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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. |
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2. **Train and Evaluate Machine Learning Models:** Implement and compare the performance of several machine learning classifiers, specifically: |
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- Naive Bayes |
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- Decision Tree |
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- K-Nearest Neighbors |
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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. |
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4. **Facilitate Learning:** Serve as an educational tool for learning about network intrusion detection, machine learning classification, and dataset analysis. |
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## Installation |
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To run this app, you need to have Python installed along with the following libraries: |
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- streamlit |
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- datasets |
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- pandas |
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- huggingface_hub |
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- scikit-learn |
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- seaborn |
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- matplotlib |
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- numpy |
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- Pillow |
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You can install the required libraries using pip: |
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```bash |
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pip install streamlit datasets pandas huggingface_hub scikit-learn seaborn matplotlib numpy Pillow |
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``` |
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## Usage |
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1. Ensure you have set the `HF_TOKEN` environment variable with your Hugging Face token. |
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2. Run the Streamlit app: |
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```bash |
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streamlit run app.py |
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``` |
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## Features |
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- **Dataset Information:** View the dataset's structure, data types, and descriptive statistics. |
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- **Naive Bayes Classifier:** Train and evaluate a Naive Bayes model. |
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- **Decision Tree Classifier:** Train and evaluate a Decision Tree model. |
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- **K-Nearest Neighbor Classifier:** Train and evaluate a K-Nearest Neighbor model. |
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- **Confusion Matrix and Classification Report:** Visualize the performance of each model. |
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## Screenshots |
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!Cybersecurity |
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## License |
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This project is licensed under the MIT License. |
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## Acknowledgements |
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- The UNSW-NB15 dataset creators at the University of New South Wales, Canberra. |
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- The Hugging Face team for providing the datasets and tools. |
