Skip to content
#

one-class-svm

Here are 51 public repositories matching this topic...

isp-ddos-auto-detector

DDoS detection using anomaly detection in high-speed ITP networks. Comparing Autoencoder, Isolation Forest, Local Outlier Factor, and One-Class SVM across real ITP datasets, different aggregation windows, and feature selections using Pearson’s correlation coefficient.

  • Updated Jan 28, 2026
  • Python

Anomaly detection (also known as outlier analysis) is a data mining step that detects data points, events, and/or observations that differ from the expected behavior of a dataset. A typical data might reveal significant situations, such as a technical fault, or prospective possibilities, such as a shift in consumer behavior.

  • Updated Dec 19, 2021
  • Jupyter Notebook

The LLM Defense Framework enhances large language model security through post-processing defenses and statistical guarantees based on one-class SVM. It combines advanced sampling methods with adaptive policy updates and comprehensive evaluation metrics, providing researchers and practitioners with tools to build more secure AI systems.

  • Updated Feb 6, 2025
  • Python

Constraint-enforcing synthetic IoT packet generation. Two methods: statistical learning (PCA + dual OCSVM/IF gating, ~1,091 pkts/sec) and a genetic algorithm (composite fitness, ~5.7 pkts/sec, 0.62% anomaly). Amplifies the 5-sample ARP Spoofing class by 200x. All 12 ACI-IoT-2023 categories pass independent validators. ICCCN 2026 submission.

  • Updated Apr 17, 2026
  • Python

Improve this page

Add a description, image, and links to the one-class-svm 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 one-class-svm topic, visit your repo's landing page and select "manage topics."

Learn more