Project Overview
NetPulse is an intelligent network intrusion detection system that leverages machine learning algorithms to identify and classify malicious network traffic patterns in real-time. The system continuously analyzes network packets, detects anomalies, and alerts administrators to potential security threats.
Built as a capstone project to demonstrate the intersection of cybersecurity and artificial intelligence, NetPulse achieves 94% accuracy in detecting various attack types including DDoS, port scanning, and SQL injection attempts.
System Interface
Real-time monitoring dashboard with threat detection metrics
Instant threat alerts with actionable response options
Key Features
Real-Time Analysis
Continuous monitoring and analysis of network traffic with sub-second detection latency
ML-Powered Detection
Random Forest classifier trained on 100,000+ labeled network flows for accurate threat identification
Alert System
Customizable alert thresholds with email and webhook notifications for security teams
Dashboard Analytics
Web-based dashboard for visualizing threats, attack trends, and network statistics
Technology Stack
Backend & ML
- Python 3.11
- Scikit-learn (Random Forest)
- Scapy for packet capture
- Pandas & NumPy
Frontend
- Flask web framework
- Chart.js for visualizations
- Bootstrap 5
- WebSockets for real-time updates
Data & Storage
- SQLite for alerts
- Redis for caching
- CICIDS2017 dataset
- CSV export functionality
Challenges & Solutions
Challenge: High False Positive Rate
Initial implementation produced too many false alarms, overwhelming administrators and reducing trust in the system.
Solution: Implemented feature engineering to extract 28 key packet features, applied hyperparameter tuning with GridSearchCV, and introduced confidence thresholds. Reduced false positives by 67%.
Challenge: Performance at Scale
Processing 10,000+ packets per second caused significant CPU bottlenecks and delayed threat detection.
Solution: Implemented multi-threading for packet capture and analysis, optimized feature extraction pipeline, and added batch processing. Achieved 15x performance improvement.
Challenge: Model Generalization
Model performed well on training data but struggled with real-world network traffic patterns not in the dataset.
Solution: Collected custom network samples from homelab, implemented ensemble methods combining multiple classifiers, and added anomaly detection for unknown attack types.
Results & Impact
Future Enhancements
- Deep learning models (LSTM) for sequence-based attack detection
- Integration with SIEM platforms (Splunk, ELK Stack)
- Automated response system for blocking malicious IPs
- Distributed deployment across multiple network segments
- Cloud-native architecture with Kubernetes orchestration
Interested in This Project?
Check out the code on GitHub or get in touch to discuss the implementation details