UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.

A distributed Spark/Scala implementation of the isolation forest algorithm for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.

Isolation Forest on Spark

(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

This research project will illustrate the use of machine learning and deep learning for predictive analysis in industry 4.0.

offical implementation of TKDE paper "Deep isolation forest for anomaly detection"

⭐ An anomaly-based intrusion detection system.

This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets

implement the machine learning algorithms by python for studying

Implementation of feature engineering from Feature engineering strategies for credit card fraud

C++, rust, julia, python2, and python3 implementations of the Isolation Forest anomaly detection algorithm.

Isolation forest implementation in Go

Security Analytics Engine - Anomaly Detection in Web Traffic

Official repository of the paper "Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance", M. Carletti, M. Terzi, G. A. Susto.

An implementation of Isolation forest

Using Unsupervised methods to identify anomalies in user behaviour through IP Profiling

Web Crawler Detection using Unsupervised Algorithms

Rust port of the extended isolation forest algorithm for anomaly detection

Combination Robust Cut Forests: Merging Isolation Forests and Robust Random Cut Forests

Surface water quality data analysis and prediction of Potomac River, West Virginia, USA. Using time series forecasting, and anomaly detection : ARIMA, SARIMA, Isolation Forest, OCSVM and Gaussian Distribution