A powerful and comprehensive framework for real-time threat detection and defense.
The Deep Detect is an advanced, modular framework designed to monitor and secure systems in real-time. This project leverages state-of-the-art techniques, including machine learning models, file integrity monitoring, network activity scanning, and automated alerts.
Whether you're a cybersecurity professional or developer, this toolkit provides a scalable, extensible solution to detect anomalies, evaluate vulnerabilities, and respond to potential threats effectively.
- Real-Time Monitoring:
- System metrics (CPU, memory, and disk usage).
- Network connection risk assessment.
- Machine Learning-Driven Anomaly Detection:
- Models: Isolation Forest, One-Class SVM.
- Synthetic data scaling and anomaly flagging.
- File Integrity Monitoring:
- Critical files' hash-based monitoring and automated alerts on violations.
- Vulnerability Scanning:
- OS-specific vulnerability detection (Windows, Linux, macOS).
- Network-based vulnerability assessment.
- Automated Alerts:
- Email notifications for critical events, threat levels, and anomalies.
- Multi-Threaded Architecture:
- Continuous security monitoring across system, network, file integrity, and vulnerability domains.
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Clone the Repository:
git clone https://github.com/yesh00008/DeepDetect/ cd DeepDetect -
Install Dependencies
pip install -r requirements.txt
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Run the Toolkit
python main.py
- Real-world dataset integration
- Expanded vulnerability databases
- Anomaly trend visualization
- Cloud monitoring support
Contributions are welcome! For more information or to contribute, contact Yashwanth Thotakura at thotakurayaswanth104@gmail.com.
This project is licensed under the MIT License.