Physics-Informed Graph Neural Networks for Attack Path Prediction

Marin François, Pierre-Emmanuel Arduin and Myriam Merad
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Marin François: LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique
Pierre-Emmanuel Arduin: DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique
Myriam Merad: LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique

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Abstract: The automated identification and evaluation of potential attack paths within infrastructures is a critical aspect of cybersecurity risk assessment. However, existing methods become impractical when applied to complex infrastructures. While machine learning (ML) has proven effective in predicting the exploitation of individual vulnerabilities, its potential for full-path prediction remains largely untapped. This challenge stems from two key obstacles: the lack of adequate datasets for training the models and the dimensionality of the learning problem. To address the first issue, we provide a dataset of 1033 detailed environment graphs and associated attack paths, with the objective of supporting the community in advancing ML-based attack path prediction. To tackle the second, we introduce a novel Physics-Informed Graph Neural Network (PIGNN) architecture for attack path prediction. Our experiments demonstrate its effectiveness, achieving an F1 score of 0.9308 for full-path prediction. We also introduce a self-supervised learning architecture for initial access and impact prediction, achieving F1 scores of 0.9780 and 0.8214, respectively. Our results indicate that the PIGNN effectively captures adversarial patterns in high-dimensional spaces, demonstrating promising generalization potential towards fully automated assessments.

Keywords: Attack path prediction; Deep learning; Physics-informed neural networks; Graph neural networks (search for similar items in EconPapers)
Date: 2025-04-10
New Economics Papers: this item is included in nep-big and nep-cmp
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Published in Journal of Cybersecurity and Privacy, 2025, 5 (2)

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