ZeroDay-SentinalAI is a framework powered by AI with the potential to revolutionize cybersecurity by addressing one of the most challenging aspects of threat detection—unknown vulnerabilities.

The framework consists of six key components that work together to detect and mitigate zero-day attacks:

• Data Collection Module: Gathers system metrics to establish behavioral baselines.
• AI Model Training Module: Implements multiple AI algorithms for anomaly detection.
• Real-time Monitoring Module: Continuously checks system behavior against the baseline.
• Response Engine: Automatically mitigates detected threats.
• Web Dashboard: Provides real-time visualization of system security status.
• Framework Integration: Ties all components together into a cohesive system.

This framework leverages multiple AI approaches for comprehensive security:

• Isolation Forest: Efficiently detects outliers by randomly selecting features and isolating observations.
• DBSCAN Clustering: Identifies dense regions of normal behavior and flags outliers.

• Autoencoder Neural Networks: Learns the normal system behavior pattern and identifies deviations by measuring reconstruction error.

• Uses a consensus-based approach combining multiple detection models to reduce false positives.

• Implements normalized scoring across different detection methods to quantify the severity of potential threats.

• Uses clustering techniques to group similar incidents for pattern recognition.

Ensure you have the following installed:

• Python 3.8+
• Required packages: pandas, numpy, scikit-learn, tensorflow, dash, plotly, psutil

Clone the repository and install dependencies:

# Clone the repository
git clone https://github.com/RiturajSingh2004/ZeroDay-SentinalAI.git
cd ZeroDay-SentinalAI

# Install dependencies
pip install pandas==2.0.0 numpy==1.24.0 scikit-learn==1.3.0 tensorflow==2.17.0 keras==3.6.0 psutil==5.9.5 plotly==5.18.0 dash==2.14.0 dash-bootstrap-components==1.5.0 joblib==1.3.0 requests==2.31.0

python main.py --collect

python main.py --train

python main.py --monitor --respond

python main.py --dashboard

python main.py --all

• Reinforcement Learning: Implement reinforcement learning for adaptive response strategies.
• Federated Learning: Enable secure knowledge sharing across multiple deployments.
• Explainable AI Components: Add interpretability to anomaly detection results.
• Adversarial Training: Improve robustness against evasion techniques.
• Transfer Learning: Apply knowledge from known attacks to detect novel variations.

• Data Privacy: The framework processes only system metrics, not content data.
• Attack Surface: The dashboard should be deployed on a secure network.
• False Positives: Thresholds are customizable to balance security and usability.

This project is licensed under the MIT License. See the LICENSE file for details.

Contributions are welcome! Please open an issue or submit a pull request.