Awesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contribut…

An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection

This repository contains a reading list of papers on multivariate time series anomaly detection. This repository is still being continuously improved.

[CIKM 2021] A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning".

Anomaly detection method that incorporates multi-scale features to sparse coding

Semi-supervised anomaly detection method

Detects anomalous resting heart rate from smartwatch data.

This project provides a time series anomaly detection algorithm based on the dynamic threshold generation model.

The implementation of the paper Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors

Several examples of anomaly detection algorithms for time series data.

Nonnegative-Constrained Joint Collaborative Representation With Union Dictionary for Hyperspectral Anomaly Detection

The paper "Deep Graph Level Anomaly Detection with Contrastive Learning" has been accepted by Scientific Reports Journal.

An official source code for paper "Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning", accepted by ACM MM 2023.

OCR to detect and recognize dot-matrix text written with inkjet-printed on medical PVC bag

Uses LSTM-based autoencoders to detect abnormal resting heart rate during the coronavirus (SARS-CoV-2) infectious period using the wearables data.

Multivariate distributions for hyperspectral anomaly detection based on autoencoder

Anomaly detection from ships' Automatic Identification System (AIS) data

Anomaly Detection deployed on machine data dataset for Predictive Maintenance

Methodology for anomaly detection on multivariate streams using path signatures and the variance norm.

This project focuses on network anomaly detection due to the exponential growth of network traffic and the rise of various anomalies such as cyber attacks, network failures, and hardware malfunctions. This project implement clustering algorithms from scratch, including K-means, Spectral Clustering, Hierarchical Clustering, and DBSCAN