Neuro-Symbolic AI Framework for Intelligent Privacy-Preserving Cybersecurity in Federated Cloud Systems

Abstract

The rapid adoption of federated cloud systems has created new opportunities for collaborative artificial intelligence while simultaneously introducing significant cybersecurity and privacy challenges. Conventional machine learning approaches can detect complex threats but often lack interpretability, contextual reasoning, and explicit security knowledge. At the same time, centralized learning requires sensitive data to be transferred to common repositories, creating risks of privacy violations and data exposure. This paper proposes a Neuro-Symbolic AI Framework for Intelligent Privacy-Preserving Cybersecurity in Federated Cloud Systems that combines neural learning, symbolic reasoning, federated learning, privacy-preserving mechanisms, and intelligent threat detection. The framework enables participating cloud nodes to train local neural models without directly sharing raw security data while using symbolic rules and knowledge representations to improve explainability and security decision-making. The proposed methodology incorporates distributed model training, secure aggregation, differential privacy, anomaly detection, knowledge graphs, rule-based reasoning, and adaptive threat response. Neural components identify hidden patterns in network traffic, authentication events, system logs, and cloud activities, while symbolic components validate detected behaviors against security policies and known threat relationships. The framework is designed to improve privacy, detection accuracy, interpretability, cross-cloud collaboration, and resilience against evolving cyber threats. The research establishes a comprehensive methodology for integrating learning and reasoning within federated cloud security environments and provides a foundation for trustworthy, adaptive, and privacy-aware cybersecurity intelligence.