КИБ-2025

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https://openrepository.mephi.ru/handle/123456789/40355

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Просмотр КИБ-2025 по Автор "TROFIMOV, A. G."

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    HIERARCHICAL GAUSSIAN PROCESSES AND STOCHASTIC PAC-BAYESIAN TRANSFORMERS FOR UNCERTAINTYCALIBRATED INTRUSION DETECTION ACROSS CLOUD AND ENTERPRISE NETWORKS
    (НИЯУ МИФИ, 2025) ANAEDEVHA, R. N.; TROFIMOV, A. G.; Трофимов, Александр Геннадьевич
    We propose a unified uncertainty quantification framework for network intrusion detection systems (NIDS) across heterogeneous environments. The system employs (i) hierarchical Gaussian processes (HGP) with adversarially-robust inducing points for cloud datasets and (ii) stochastic Probably Approximately Correct (PAC)-Bayesian transformers for general NIDS datasets. Both pipelines sustain >94% accuracy under adversarial attacks, and provide epistemic uncertainty for risk-based triage.
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    STOCHASTIC GAME-THEORETIC FEDERATED LEARNING AND SELECTIVE STATE-SPACE MODELS FOR MULTI-CLOUD AND ENTERPRISE NETWORK INTRUSION DETECTION
    (НИЯУ МИФИ, 2025) ANAEDEVHA ,R. N.; TROFIMOV, A. G.; Трофимов, Александр Геннадьевич
    We propose a dual-branch architecture for cross-domain intrusion detection systems across six datasets. The cloud branch employs game-theoretic federated learning (FL) with Byzantine robustness and differential privacy (DP) guarantees (Edge-IIoT, Container, SOC), achieving 95.7–96.9% accuracy. The system maintains ε-DP, Byzantine resilience, and adversarial robustness via progressive adversarial robust distillation (PARD) and Probably Approximately Correct (PAC)-Bayesian regularization.