anomaly-detection

Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.

Out-of-distribution detection, robustness, and generalization resources. The repository contains a curated list of papers, tutorials, books, videos, articles and open-source libraries etc

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

A robust streaming log template miner based on the Drain algorithm

Official implementation of "Segment Any Anomaly without Training via Hybrid Prompt Regularization (SAA+)".

MOMENT: A Family of Open Time-series Foundation Models, ICML'24

Luminaire is a python package that provides ML driven solutions for monitoring time series data.

Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.

Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)

[VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.

Resources for working with time series and sequence data

Python module for hyperspectral image processing

A unified multi-task time series model.

OpenChatBI is an intelligent chat-based BI tool powered by large language models, designed to help users query, analyze, and visualize data through natural language conversations. It uses LangGraph and LangChain to build chat agent and workflows that support natural language to SQL conversion and data analysis.

Real Intelligence Threat Analytics (RITA) is a framework for detecting command and control communication through network traffic analysis.

Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".

Real-time anomaly detection for time series metrics. 11 years of production anomaly detection evolution. Multi-algorithm ensembles. Pattern recognition using semi-supervised and unsupervised learning. Correlation analysis. Learns and gets better over time. No GPU required, can run on a VPS.

Deep learning-based outlier/anomaly detection

A comprehensive survey on the time series domains

This repository contains a reading list of papers on Time Series Segmentation. This repository is still being continuously improved.

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Timely detections for more proactive and effective actions in offshore oil wells!

[CVPR 2025] Official Implementation of "Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection". The first multi-class UAD model that can compete with single-class SOTAs

A robust, and flexible open source User & Entity Behavior Analytics (UEBA) framework used for Security Analytics. Developed with luv by Data Scientists & Security Analysts from the Cyber Security Industry. [BETA]