https://github.com/d-ailin/GDN

Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021)
https://github.com/d-ailin/GDN

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

• Host: GitHub
• URL: https://github.com/d-ailin/GDN
• Owner: d-ailin
• License: mit
• Created: 2021-02-09T06:21:42.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2023-07-28T03:00:05.000Z (over 1 year ago)
• Last Synced: 2024-06-14T01:26:19.242Z (6 months ago)
• Language: Python
• Homepage:
• Size: 386 KB
• Stars: 444
• Watchers: 5
• Forks: 139
• Open Issues: 41
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# GDN

Code implementation for : [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series(AAAI'21)](https://arxiv.org/pdf/2106.06947.pdf)

# Installation

### Requirements

* Python >= 3.6

* cuda == 10.2

* [Pytorch==1.5.1](https://pytorch.org/)

* [PyG: torch-geometric==1.5.0](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)

### Install packages

```

    # run after installing correct Pytorch package

    bash install.sh

```

### Quick Start

Run to check if the environment is ready

```

    bash run.sh cpu msl

    # or with gpu

    bash run.sh  msl    # e.g. bash run.sh 1 msl

```

# Usage

We use part of msl dataset(refer to [telemanom](https://github.com/khundman/telemanom)) as demo example. 

## Data Preparation

```

# put your dataset under data/ directory with the same structure shown in the data/msl/

data

 |-msl

 | |-list.txt    # the feature names, one feature per line

 | |-train.csv   # training data

 | |-test.csv    # test data

 |-your_dataset

 | |-list.txt

 | |-train.csv

 | |-test.csv

 | ...

```

### Notices:

* The first column in .csv will be regarded as index column. 

* The column sequence in .csv don't need to match the sequence in list.txt, we will rearrange the data columns according to the sequence in list.txt.

* test.csv should have a column named "attack" which contains ground truth label(0/1) of being attacked or not(0: normal, 1: attacked)

## Run

```

    # using gpu

    bash run.sh  

    # or using cpu

    bash run.sh cpu 

```

You can change running parameters in the run.sh.

# Others

SWaT and WADI datasets can be requested from [iTrust](https://itrust.sutd.edu.sg/)

# Citation

If you find this repo or our work useful for your research, please consider citing the paper

```

@inproceedings{deng2021graph,

  title={Graph neural network-based anomaly detection in multivariate time series},

  author={Deng, Ailin and Hooi, Bryan},

  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},

  volume={35},

  number={5},

  pages={4027--4035},

  year={2021}

}

```