Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values

KDD 2019: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

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.

Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021)

Applied generative adversarial networks (GANs) to do anomaly detection for time series data

Offical implementation of "RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection (CVPR 2024)"

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

ISSRE'20: Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks

A thesis submitted for the degree of Master of Science in Computer Networks and Security

AI deep learning neural network for anomaly detection using Python, Keras and TensorFlow

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

"Rethinking Graph Neural Networks for Anomaly Detection" in ICML 2022

Suricata rules for network anomaly detection

Example code for neural-network-based anomaly detection of time-series data (uses LSTM)

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

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

Source code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot anomaly detection, semi-supervised anomaly detection

基于神经网络的流量异常检测

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We used generative adversarial networks (GANs) to do anomaly detection for time series data.

Code for Deep Anomaly Detection on Attributed Networks (SDM2019)

A Vision Transformer Network for Image Anomaly Detection and Localization

Implementation of MTAD-GAT: Multivariate Time-series Anomaly Detection via Graph Attention Network

[TNNLS] Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

Unsupervised Anomaly Detection with Generative Adversarial Networks on MIAS dataset

System for network traffic analysis and anomaly detection.

AI & Machine Learning: Detection and Classification of Network Traffic Anomalies based on IoT23 Dataset

Official PyTorch implementation of the paper “Explainable Deep Few-shot Anomaly Detection with Deviation Networks”, weakly/partially supervised anomaly detection, few-shot anomaly detection, image defect detection.

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.