An End-to-end Outlier Detection System

Anomaly detection related books, papers, videos, and toolboxes

Anomaly detection library based on singular spectrum transformation(sst)

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

STUMPY is a powerful and scalable Python library for modern time series analysis

Resources for working with time series and sequence data

A large collection of system log datasets for AI-powered log analytics

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.

WinDBG Anti-RootKit Extension

Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

RNN based Time-series Anomaly detector model implemented in Pytorch.

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

Detect EEG artifacts, outliers, or anomalies using supervised machine learning.

Healthbot configuration examples. Scripts to manage Healthbot. Closed loop automation. Healthbot building blocks description and troubleshooting guide

An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana

A Python toolkit for rule-based/unsupervised anomaly detection in time series

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

A log analysis toolkit for automated anomaly detection [ISSRE'16]