https://github.com/hannahgsimon/ddos-ml-mitigation-simulation
Simulation-based analysis of DDoS mitigation strategies using machine learning and adaptive load balancing. This project evaluates both detection accuracy and system-level performance (throughput, latency, and server health) across multiple attack scenarios, using Random Forest, Logistic Regression, and baseline models with cross-validation.
https://github.com/hannahgsimon/ddos-ml-mitigation-simulation
anomaly-detection cybersecurity data-science intrusion-detection load-balancing machine-learning network-security random-forest simulation
Last synced: 21 days ago
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Simulation-based analysis of DDoS mitigation strategies using machine learning and adaptive load balancing. This project evaluates both detection accuracy and system-level performance (throughput, latency, and server health) across multiple attack scenarios, using Random Forest, Logistic Regression, and baseline models with cross-validation.
- Host: GitHub
- URL: https://github.com/hannahgsimon/ddos-ml-mitigation-simulation
- Owner: hannahgsimon
- License: mit
- Created: 2026-04-27T09:33:04.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2026-04-27T09:41:51.000Z (about 2 months ago)
- Last Synced: 2026-04-27T11:21:49.776Z (about 2 months ago)
- Topics: anomaly-detection, cybersecurity, data-science, intrusion-detection, load-balancing, machine-learning, network-security, random-forest, simulation
- Language: Python
- Homepage:
- Size: 333 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0