Evaluation of Anomaly Detection for Cybersecurity Using Inductive Node Embedding with Convolutional Graph Neural Networks

Authors

A Abou Rida, P Parrend, R Amhaz

Where

Complex Network 2021

Type

inproceedings

Date

2021-10-01

Abstract

In the face of continuous cyberattacks, many scientists have proposed machine learning-based network anomaly detection methods. While deep learning effectively captures unseen patterns of Euclidean data, there is a huge number of applications where data are described in the form of graphs. Graph analysis have improved detecting anomalies in non-Euclidean domains, but it suffered from high computational cost. Graph embeddings have solved this problem by converting each node in the network into low dimensional representation, but it lacks the ability to generalize to unseen nodes. Graph convolution neural network methods solve this problem through inductive node embedding (inductive GNN). Inductive GNN shows better performance in detecting anomalies with less complexity than graph analysis and graph embedding methods.

Bibtex (lrde.bib)

@InProceedings{	  rida.21.cn,
  author	= {Abou Rida, A. and Parrend, P. and Amhaz, R.},
  title		= {Evaluation of Anomaly Detection for Cybersecurity Using
		  Inductive Node Embedding with Convolutional Graph Neural
		  Networks},
  booktitle	= {Complex Network 2021},
  month		= oct,
  year		= {2021},
  abstract	= {In the face of continuous cyberattacks, many scientists
		  have proposed machine learning-based network anomaly
		  detection methods. While deep learning effectively captures
		  unseen patterns of Euclidean data, there is a huge number
		  of applications where data are described in the form of
		  graphs. Graph analysis have improved detecting anomalies in
		  non-Euclidean domains, but it suffered from high
		  computational cost. Graph embeddings have solved this
		  problem by converting each node in the network into low
		  dimensional representation, but it lacks the ability to
		  generalize to unseen nodes. Graph convolution neural
		  network methods solve this problem through inductive node
		  embedding (inductive GNN). Inductive GNN shows better
		  performance in detecting anomalies with less complexity
		  than graph analysis and graph embedding methods.},
  x-international-audience={Yes},
  x-language	= {EN},
  url		= {http://icube-publis.unistra.fr/4-APA21},
  doi		= {https://doi.org/10.1007/978-3-030-93413-2_47}
}