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

A distributed Spark/Scala implementation of the isolation forest and extended isolation forest algorithms 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"

:star: 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

Isolation forest implementation in Go

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

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.

Detect suspicious financial transactions using SQL and Python. Build user-level behavioral features in SQLite, apply Isolation Forest for anomaly detection, and visualize high-risk patterns. Demonstrates unsupervised fraud analytics and SQL-driven data science workflow.

Anomaly detection in synthetic transaction and sales data with Python. Generates realistic data, injects unusual events, and applies Isolation Forest, Local Outlier Factor, and Z-score methods to detect outliers. Produces anomaly reports and visualizations for portfolio-ready demonstration of data science skills.

An implementation of Isolation forest

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

Web Crawler Detection using Unsupervised Algorithms

Functions and example notebooks for using/comparing different methods of geochemical multivariate outlier detection.

Rust port of the extended isolation forest algorithm for anomaly detection

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

Machine Learning-based Intrusion Detection System (IDS) tailored for resource-constrained networks

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

Package implements decision tree and isolation forest

Anomaly detection using isolation forest

🚀 Financial Anomaly Detection with DeepSeek and Isolation Forest – A powerful, locally-run tool for detecting financial anomalies using Isolation Forest and DeepSeek LLM. Features AI-powered insights, interactive time-series visualization, and automated PDF audit reports. 🔍📊

The code for Isolation Mondrian (iMondrian) forest for batch and online anomaly detection

Extended Isolation Forests for Anomaly/Outlier Detection in R

Awesome machine learning algorithms for anomaly detection, including papers and source code