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Ranking Attack Graphs with Graph Neural Networks

  • Liang Lu
  • Rei Safavi-Naini
  • Markus Hagenbuchner
  • Willy Susilo
  • Jeffrey Horton
  • Sweah Liang Yong
  • Ah Chung Tsoi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5451)

Abstract

Network security analysis based on attack graphs has been applied extensively in recent years. The ranking of nodes in an attack graph is an important step towards analyzing network security. This paper proposes an alternative attack graph ranking scheme based on a recent approach to machine learning in a structured graph domain, namely, Graph Neural Networks (GNNs). Evidence is presented in this paper that the GNN is suitable for the task of ranking attack graphs by learning a ranking function from examples and generalizes the function to unseen possibly noisy data, thus showing that the GNN provides an effective alternative ranking method for attack graphs.

Keywords

Hide Layer Machine Learning Approach Ranking Scheme PageRank Algorithm Attack Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Liang Lu
    • 1
  • Rei Safavi-Naini
    • 2
  • Markus Hagenbuchner
    • 1
  • Willy Susilo
    • 1
  • Jeffrey Horton
    • 1
  • Sweah Liang Yong
    • 1
  • Ah Chung Tsoi
    • 3
  1. 1.University of WollongongWollongongAustralia
  2. 2.Department of Computer ScienceUniversity of CalgaryCanada
  3. 3.Hong Kong Baptist UniversityKowloonHong Kong

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