Graph Deep Learning-Driven Cyber Threat Detection and Risk Analysis Framework for Secure

Abstract

The increasing complexity of cloud infrastructures has created security environments in which cyber threats emerge from interconnected relationships among users, applications, virtual machines, services, network flows, and access privileges. Conventional security analytics frequently represent these entities as independent observations, limiting their ability to identify relational attack patterns and propagate contextual risk. This paper proposes a Graph Deep Learning-Driven Cyber Threat Detection and Risk Analysis Framework for Secure Cloud Environments that models cloud infrastructures as dynamic heterogeneous graphs and applies graph-based representation learning to identify anomalous entities, suspicious relationships, and coordinated attack behaviors. The framework integrates graph construction, feature representation, graph deep learning, threat classification, temporal reasoning, and risk prioritization into a unified security pipeline. Its theoretical foundation is strengthened by research on symbolic representations, state-space abstraction, generalized planning, temporal logic, and structured reasoning. In particular, concepts of relational representation and structural abstraction provide a basis for transforming complex cloud security states into machine-processable graph structures. The proposed framework extends graph-based cyber threat identification toward risk-aware decision support by associating detected threats with affected assets, attack relationships, and potential propagation paths. The analysis indicates that graph deep learning can provide greater contextual awareness than isolated event classification, while temporal and symbolic reasoning can improve interpretability and decision consistency. The framework is particularly relevant to cloud environments characterized by dynamic workloads, distributed services, privilege dependencies, and rapidly changing attack surfaces.