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

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 newer more updated version of the prometheus anomaly detector (https://github.com/AICoE/prometheus-anomaly-detector-legacy)

Log Anomaly Detection - Machine learning to detect abnormal events logs

Go implementation of MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection

Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

PackInspect is a low-code visual inspection tool built using TensorFlow and Streamlit. It allows users to detect defects in bottle images (or similar packaging components) by uploading images or capturing them via webcam.

Samples for the Anomaly Detection API documentation:

Cell tower logger and anomaly detector for Linux mobile devices. Monitors cellular connections, logs tower data with GPS, and detects suspicious activity.

A completely automated anomaly detector Zeek network flows files (conn.log).

Time series based anomaly detector

Contextual Anomaly Detector

Experiments on unsupervised anomaly detection using variational autoencoder. The variational autoencoder is implemented in Pytorch.

Using DNN for univariate time series anomaly detection over AIOps Competition dataset

[ICLR 2026] The implementation of the paper Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors

Anomaly detector for NodeJS

Python implementation of MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

The design of a time series anomaly detector through RNN and RL.

User anomaly detector based on logs generated by Osquery framework and machine learning to process those logs.

[NeurIPS'24] ARC: A Generalist Graph Anomaly Detector with In-Context Learning

No description available

lsanomaly, a probabilistic anomaly detector for Python.

Online Time Series Anomaly Detectors

Implementation of Kohonen SOM for anomaly detection purposes.

This repository describes the implementation of an unsupervised anomaly detector using the Anomalib library.

[IEEE TII 2025] Official Implementation for "Dual-Detector Reoptimization for Federated Weakly Supervised Video Anomaly Detection via Adaptive Dynamic Recursive Mapping"

A simple application to collect data from a prometheus host and train a model on it

an innovative framework for indoor air quality outlier detection, comprising three modules: LSTM-AE-based reconstruction error detector, latent feature class-assisted SVM detector, and an ensemble model for robust real-time anomaly detection. Ideal for industrial applications, providing stable and versatile outlier decision rules.

A try to autoencode an LSTM to do anomaly detection