A one class svm implementation to detect the anomalies in network.

Fraud hosts with substantial amount of fraudulent traffic using the impression logs for selected IP addresses

Detecting Malicious Commands

Canned estimators and pre-trained models converted for TensorFlow.

Insight Data Science DS.2019C.TO project

anomaly detection by one-class SVM

Fast Incremental Support Vector Data Description implemented in Python

Detecting weather anomalies for Dublin Airport

Data exploration, anomaly detection, and data generation for oil deposits dataset.

Project from seminar "Data Mining in Production"

One-Class SVMs for Document Classification

A curated list of awesome resources dedicated to One Class Classification.

Anomaly detection for Sequential dataset

This demo shows how to detect the crack images using one-class SVM using MATLAB.

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.

Code for paper 'Avoid touching your face: A hand-to-face 3d motion dataset (covid-away) and trained models for smartwatches'

Anomaly detection using IF, LOF, OC-SVM, Autoencoder.

OCS-WAF: a Web Application Firewall based on anomaly detection using One-Class SVM classifier

This repository provides some recommender engine models.