https://github.com/lasara26/attack-detection-with-machine-learning
This project focuses on detecting cyber attacks using machine learning techniques. It employs various algorithms to analyze network traffic and identify potential threats in real-time.
https://github.com/lasara26/attack-detection-with-machine-learning
cybersecurity d3 docker-image feature-extraction feature-selection genetic-algorithm lidarometry log-forensics network-traffic networksecurity phishing phishing-attacks threat-detection yara
Last synced: 4 months ago
JSON representation
This project focuses on detecting cyber attacks using machine learning techniques. It employs various algorithms to analyze network traffic and identify potential threats in real-time.
- Host: GitHub
- URL: https://github.com/lasara26/attack-detection-with-machine-learning
- Owner: Lasara26
- License: mit
- Created: 2025-04-11T17:39:31.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-10-05T02:32:00.000Z (4 months ago)
- Last Synced: 2025-10-05T04:24:48.992Z (4 months ago)
- Topics: cybersecurity, d3, docker-image, feature-extraction, feature-selection, genetic-algorithm, lidarometry, log-forensics, network-traffic, networksecurity, phishing, phishing-attacks, threat-detection, yara
- Language: Python
- Size: 2.37 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🚨 Attack Detection with Machine Learning 🚨
Welcome to the **Attack Detection with Machine Learning** repository. This project focuses on identifying malicious traffic in network systems using machine learning techniques. By leveraging various algorithms, particularly the Random Forest classifier, this tool automates the detection of network threats.
## 📁 Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Technologies Used](#technologies-used)
- [Installation](#installation)
- [Usage](#usage)
- [How It Works](#how-it-works)
- [Report Generation](#report-generation)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## 📜 Introduction
In today's digital landscape, cybersecurity is crucial. Cyber threats evolve rapidly, making it essential to have robust systems in place for detection and response. This project aims to provide a solution for detecting attacks in network traffic through machine learning.
The system analyzes traffic patterns and identifies anomalies that may indicate malicious activity. By using machine learning, it learns from past data to improve its detection capabilities over time.
## ✨ Features
- **Automated Detection**: Automatically identifies malicious traffic patterns.
- **Classification**: Classifies different types of attacks.
- **Interactive Menu**: User-friendly interface for easy navigation.
- **JSON Report**: Generates detailed reports in JSON format for further analysis.
- **Machine Learning Algorithms**: Utilizes Random Forest for accurate threat detection.
- **Network Security**: Enhances the security of network systems.
## 🛠️ Technologies Used
This project incorporates various technologies and tools, including:
- **Python**: The primary programming language for development.
- **Machine Learning Libraries**:
- `scikit-learn` for implementing machine learning algorithms.
- `pandas` for data manipulation.
- `numpy` for numerical computations.
- **Visualization**:
- `matplotlib` and `seaborn` for visualizing data and results.
- **JSON**: For report generation and data interchange.
## 📥 Installation
To set up the project on your local machine, follow these steps:
1. **Clone the Repository**:
```bash
git clone https://github.com/Lasara26/Attack-Detection-with-Machine-Learning.git
cd Attack-Detection-with-Machine-Learning
```
2. **Install Required Packages**:
Make sure you have Python installed. Then, run:
```bash
pip install -r requirements.txt
```
3. **Download and Execute**:
For the latest release, visit [Releases](https://github.com/Lasara26/Attack-Detection-with-Machine-Learning/releases) to download the necessary files.
## ⚙️ Usage
Once you have installed the project, you can start using it. Run the main script:
```bash
python main.py
```
You will see an interactive menu guiding you through the available options. You can choose to analyze network traffic or generate reports based on previous analyses.
## 🔍 How It Works
The system works by analyzing network traffic data. Here's a brief overview of the process:
1. **Data Collection**: Gather network traffic data, which can be in various formats.
2. **Preprocessing**: Clean and prepare the data for analysis.
3. **Feature Extraction**: Identify relevant features that can help in classifying traffic.
4. **Model Training**: Train the Random Forest model using historical data.
5. **Detection**: Use the trained model to detect anomalies in real-time traffic.
6. **Reporting**: Generate a JSON report summarizing the findings.
## 📊 Report Generation
After running the detection, the system generates a JSON report. This report includes:
- Detected threats
- Classification of attacks
- Recommendations for mitigation
You can find the report in the `reports` directory after execution.
## 🤝 Contributing
We welcome contributions from the community. If you would like to contribute, please follow these steps:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Make your changes.
4. Commit your changes (`git commit -m 'Add some feature'`).
5. Push to the branch (`git push origin feature-branch`).
6. Open a pull request.
## 📄 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## 📫 Contact
For questions or feedback, please reach out:
- **Email**: [your-email@example.com](mailto:your-email@example.com)
- **GitHub**: [Lasara26](https://github.com/Lasara26)
For the latest releases, please check the [Releases section](https://github.com/Lasara26/Attack-Detection-with-Machine-Learning/releases).
Thank you for visiting the **Attack Detection with Machine Learning** repository! Your contributions and feedback are valuable to us.