GDN

This repo extends the Code implementation for : Graph Neural Network-Based Anomaly Detection in Multivariate Time Series(AAAI'21) by including a visualization code for the Figure 3 (left) of the paper.

Since many users have raised this issue in the original repo, I decided to address it using my own script and sharing it here.

You can find the visualization script in the Visualization folder.

Installation

Requirements

Python >= 3.6
cuda == 10.2
Pytorch==1.5.1
PyG: torch-geometric==1.5.0

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 <gpu_id> msl    # e.g. bash run.sh 1 msl

Usage

We use part of msl dataset(refer to 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 <gpu_id> <dataset>

    # or using cpu
    bash run.sh cpu <dataset>

You can change running parameters in the run.sh.

Others

SWaT and WADI datasets can be requested from iTrust

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}
}

shahaamirbader/GDN

GDN