Communication Dans Un Congrès Année : 2024

Latent States: Model-Based Machine Learning Perspectives on Cyber Resilience

Résumé

This paper presents a novel approach for modeling networks cyber resilience when undergoing cyberattack. We offer to use Model-Based Machine Learning (MBML) to address the limitations of scenario specific disruption modeling, the latter being inadequate for the evolving and highly uncertain nature of cyber threats. As a proof of concept, we collected and analyzed 300.272 signals for 9 security incidents over 30 days. We used a multinomial Hidden Markov Model for disruption response function parameters inference and latent state filtering. Along with selected signals, these are used as features to train a Random Forest in a multistep prediction task, where we predict occurrence of cyberattacks on next time-step, reaching an F1-Score of. 98 on next-hour horizon. Our hybrid model uses large-scale cyberattack telemetry as ground-truth, with the ability to produce both resilience metrics and cyberattack early warning. All algorithmic design choices were motivated by explainability and robustness trade-off.

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Dates et versions

hal-04893698 , version 1 (17-01-2025)

Identifiants

Citer

Marin François, Pierre-Emmanuel Arduin, Myriam Merad. Latent States: Model-Based Machine Learning Perspectives on Cyber Resilience. IEEE 4th Intelligent Cybersecurity Conference (ICSC), Sep 2024, Valencia, Spain. ⟨10.1109/ICSC63108.2024.10894907⟩. ⟨hal-04893698⟩
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