Open Access

Autonomous Resilience: Integrating Generative AI-Driven Threat Detection with Adaptive Query Optimization in Distributed Ecosystems

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Tang Shu Qi ¹ ,

⁴ School of Computing, National University of Singapore (NUS), Singapore

Abstract

Background: Modern digital infrastructures face a dual challenge: maintaining high-velocity data processing in distributed databases while defending against increasingly sophisticated, AI-driven cyber threats. Traditional security overlays often create throughput bottlenecks, forcing organizations to trade performance for protection.

Methods: This study presents "Autonomous Resilience," a unified framework that integrates a Generative Adversarial Network (GAN)-based Intrusion Detection System (IDS) with a machine learning-driven query optimizer. Utilizing a hybrid architecture, the system dynamically adjusts database partitioning and query routing based on real-time threat probability scores. We evaluated the framework using a synthetic environment mimicking Industry 5.0 protocols, measuring both threat detection accuracy and query latency under heavy load.

Results: The proposed GAN-based model achieved a 98.4% detection rate for zero-day anomalies, outperforming traditional supervised learning methods. Simultaneously, the adaptive query optimizer maintained a 15% reduction in latency during active scanning periods compared to static baselines.

Conclusion: The integration of generative AI for security and adaptive algorithms for data management offers a viable path toward self-healing, resilient digital ecosystems. However, the implementation requires careful consideration of uncertainty quantification and the evolving role of human operators in the loop.

Keywords

Generative Adversarial Networks, Distributed Database Optimization, Network Intrusion Detection, Industry 5.0

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Autonomous Resilience: Integrating Generative AI-Driven Threat Detection with Adaptive Query Optimization in Distributed Ecosystems

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