ГРАФИЧЕСКИЕ НЕЙРОННЫЕ СЕТИ КАК ИНСТРУМЕНТ МОНИТОРИНГА И ОБНАРУЖЕНИЯ АНОМАЛИЙ В ОБЛАЧНЫХ СИСТЕМАХ

Аномалии в облачной инфраструктуре приводят к значительным простоям и финансовым потерям. Традиционные методы обнаружения аномалий не способны улавливать сложные зависимости в микросервисных архитектурах. В данной статье представлена новая концепция темпорально-внимательного графового автоэнкодера (TAGAE) для обнаружения аномалий в облаке, использующая графовые нейронные сети (GNN) для моделирования топологических отношений и временной динамики. Наш метод объединяет многоисточниковую телеметрию (журналы, метрики, трассировки) в единую графовую структуру, использует слои усиления аномалий для повышения чувствительности и применяет фокальную функцию потерь для смягчения дисбаланса данных. При оценке на наборах данных Azure-DIAD и GCP модель TAGAE достигает F1-оценки 94,2% и AUC-PR 96,5%, снижая задержку обнаружения на 63% по сравнению с GraphSAGE. Мы также анализируем устойчивость при 40% шума и пропущенных данных и предлагаем федеративные GNN для развертывания с сохранением конфиденциальности.

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