The Network Security Intrusion Detection ML System is a Machine Learning-based application designed to detect malicious network activity and classify traffic as either normal or attack.
This project demonstrates the design, implementation, and documentation of a scalable intrusion detection system using modern development and ML practices.
- Detect malicious network traffic using ML models
- Reduce false positives
- Provide real-time classification
- Visualize attack insights
- Maintain scalable and modular architecture
Traditional security systems struggle to detect evolving cyber threats and zero-day attacks effectively. This project aims to build an intelligent detection system that can analyze network traffic data and identify anomalies using Machine Learning techniques.
- Python
- Pandas
- NumPy
- Scikit-learn
- Python / Flask (or FastAPI)
- HTML
- CSS
- JavaScript
- Docker
- AWS EC2
- AWS ECR
- Git & GitHub
NetworkSecurity/
│
├── PRD.MD # Product Requirements Document
├── PrincePRD.pdf # PDF version of PRD
├── SystemDesign.MD # High-Level & Low-Level Design
├── UserFlow.MD # User interaction flow
├── WireFrame.MD # UI wireframes
├── assets/ # Diagrams and images
└── README.MD # Project overview
- Dataset Upload (CSV-based network traffic)
- Data Preprocessing
- Model Training & Evaluation
- Intrusion Detection Classification
- Attack Logging
- Visualization Dashboard
- Accuracy
- Precision
- Recall
- F1-Score
- False Positive Rate
- 📄 Product Requirements: See
PRD.MD - 🏗 System Architecture: See
SystemDesign.MD - 🔄 User Flow: See
UserFlow.MD - 🧩 Wireframes: See
WireFrame.MD
- Real-time packet capture integration
- Deep learning-based anomaly detection
- Automated model retraining
- Cloud-native deployment
- SIEM integration
Prince Maurya
B.Tech – Computer Science
Full Stack & Machine Learning Developer
Project under development as part of OJT / Academic Submission.