Skip to content
#

noise-injection

Here are 14 public repositories matching this topic...

Noise Injection Techniques provides a comprehensive exploration of methods to make machine learning models more robust to real-world bad data. This repository explains and demonstrates Gaussian noise, dropout, mixup, masking, adversarial noise, and label smoothing, with intuitive explanations, theory, and practical code examples.

  • Updated Nov 15, 2025

A modular IDS leveraging multi-sensor correlation, sliding-window analysis, statistical Z-score anomaly detection, EMA-based SYN flood detection, and rule-based heuristics. Features deduplication, noise resilience, and severity control, enabling accurate real-time detection of scans, brute-force attacks, and multi-stage intrusions.

  • Updated Apr 14, 2026
  • Python

Improve this page

Add a description, image, and links to the noise-injection topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the noise-injection topic, visit your repo's landing page and select "manage topics."

Learn more