An AI-powered tool to predict and prevent zero-day attacks on firewalls, like Palo Alto’s CVE-2024-3400. Uses Python, Wireshark, MITRE ATT&CK datasets, and Docker for real-time anomaly detection.# Zero-Day-Vulnerability-Exploitation-Detection-Tool
An AI-powered tool to predict and prevent zero-day attacks on firewalls, like Palo Alto’s CVE-2024-3400. Uses Python, Wireshark, MITRE ATT&CK datasets, and Docker for real-time anomaly detection.
# Zero-Day Vulnerability Exploitation Detection Tool
## 📑 Table of Contents
- [**Project Description**](#project-description)
- [**Objectives**](#objectives)
- [**Why This Project**](#why-this-project)
- [**Technologies and Tools**](#technologies-and-tools)
- [**Project Structure**](#project-structure)
- [**Timeline**](#timeline)
- [**Setup Instructions**](#setup-instructions)
- [**Running the Project**](#running-the-project)
- [**Testing the Tool**](#testing-the-tool)
- [**How to Contribute**](#how-to-contribute)
- [**License**](#license)
- [**Contact**](#contact)
- [**References**](#references)
---
## 🔍 **Project Description**
This repository hosts an **AI-powered tool** designed to predict and prevent **zero-day attacks** targeting firewalls, such as Palo Alto’s CVE-2024-3400. Zero-day vulnerabilities are exploited before patches are available, posing severe risks to network security.
The tool leverages **machine learning** to analyze network traffic, detect anomalous patterns, and block potential breaches in real-time. Built with **Python**, **Wireshark**, **MITRE ATT&CK datasets**, and **Docker**, it offers a robust solution for proactive cybersecurity. The project includes data collection, model training, tool integration, and testing in a sandbox environment—ideal for enthusiasts in **networking**, **AI**, and **cybersecurity**.
---
## 🎯 **Objectives**
- Research zero-day vulnerabilities in firewalls.
- Collect and label network traffic data for training an AI model.
- Develop a machine learning model to detect potential zero-day attacks.
- Integrate the model into a functional tool for real-time monitoring.
- Test the tool in a safe environment using simulated attacks.
- Document the process and present findings.
---
## ❓ **Why This Project**
Zero-day vulnerabilities are a critical cybersecurity threat, as traditional defenses often fail against unknown exploits. This project leverages machine learning to proactively detect attack patterns, enhancing firewall security. It builds on interests in **networking** and **CTF challenges**, offering a **practical learning experience** in AI and cybersecurity.
---
## 🛠️ **Technologies and Tools**
| **Tool/Library** | **Purpose** |
|------------------|-------------|
| **Python** | AI/ML model development using libraries like `scikit-learn`, `TensorFlow`, or `PyTorch`. |
| **Wireshark** | Captures and analyzes network traffic for training data. |
| **MITRE ATT&CK Dataset** | Simulates zero-day attack patterns. |
| **Docker** | Creates a sandbox for safe testing. |
| **Python Libraries** | Listed in `requirements.txt`:<br>• `scikit-learn`: For traditional ML algorithms.<br>• `tensorflow` or `pytorch`: For deep learning models.<br>• `pandas`, `numpy`: For data processing.<br>• `matplotlib`: For visualizing results. |
---
## 🗂️ **Project Structure**
The repository is organized as follows:
---
## 🗓️ **Timeline**
| **Weeks** | **Phase** | **Tasks** |
|-----------|---------------------------|------------------------------------------------|
| 1-2 | Research & Planning | Study vulnerabilities, set up tools. |
| 3-4 | Data Collection & Labeling | Capture traffic, label data. |
| 5-6 | Feature Engineering & ML | Extract features, develop model. |
| 7-8 | Model Training & Evaluation| Train and evaluate model. |
| 9-10 | Tool Integration | Build and integrate tool. |
| 11-12 | Testing & Validation | Test in Docker environments. |
| 13-14 | Documentation & Presentation| Finalize report, prepare demo. |
---
## ⚙️ **Setup Instructions**
1. **Install Python:**
- Download Python 3.6+ from [python.org](https://www.python.org/)
- Verify:
```bash
python --version
```
2. **Install Wireshark:**
- Download from [wireshark.org](https://www.wireshark.org/)
- Follow OS-specific installation instructions.
3. **Install Docker:**
- Download from [docker.com](https://www.docker.com/)
- Verify:
```bash
docker --version
```
4. **Clone the Repository:**
```bash
git clone https://github.com/yourusername/Zero-Day-Vulnerability-Exploitation-Detection-Tool.git
cd Zero-Day-Vulnerability-Exploitation-Detection-Tool
5. **Install Python Dependencies:**
```bash
pip install -r requirements.txt
```
6. **Set Up Data**
Download MITRE ATT&CK Dataset from MITRE ATT&CK and place it in the data/ directory.
Capture network traffic using Wireshark and save it as data/capture.pcap.
## ▶️ Running the Project
### 🛡️ Data Collection
Capture traffic with Wireshark or tshark:
```bash
tshark -i eth0 -w data/capture.pcap
```
Label traffic as benign or malicious (manual or scripted).
---
### 🧹 Data Processing
```bash
python src/data_processing.py
```
---
### 🧠 Model Training
```bash
python src/model.py
```
Saves model to `models/model.pkl`.
---
### 🚦 Run the Tool
Start real-time monitoring:
```bash
python src/tool.py
```
Analyzes live traffic using the trained model.
---
## 🧪 Testing the Tool
1. Test in a Docker sandbox:
```bash
docker build -t zero-day-detector .
```
2. Run the container:
```
docker run -it zero-day-detector
```
Simulate attacks (e.g., using **Metasploit**) and verify detection.
---
## 🤝 How to Contribute
- Fork the repository.
- Create a branch:
```bash
git checkout -b feature/new-feature
```
- Commit changes:
```bash
git commit -m "Add new feature"
```
- Push the branch:
```bash
git push origin feature/new-feature
```
- Submit a pull request.
> Follow coding standards and include tests.
---
## 📄 License
This project is licensed under the **MIT License** – see `LICENSE`.
---
## 📬 Contact
Contact **Rohtih** at **rohithreddyrry2004@gmail.com** for questions or feedback.
---
## 📚 References
- [Wireshark Documentation](https://www.wireshark.org/docs/)
- [MITRE ATT&CK Framework](https://attack.mitre.org/)
- [Python Official Site](https://www.python.org/)
- [Docker Documentation](https://docs.docker.com/)
- [scikit-learn Tutorials](https://scikit-learn.org/stable/tutorial/index.html)
- [TensorFlow Tutorials](https://www.tensorflow.org/tutorials)
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