Autonomous Resilience: Integrating Generative AI-Driven Threat Detection with Adaptive Query Optimization in Distributed Ecosystems
Keywords:
Generative Adversarial Networks, Distributed Database Optimization, Network Intrusion Detection, Industry 5.0Abstract
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.
References
Prassanna R Rajgopal. (2025). AI-optimized SOC playbook for Ransomware Investigation. International Journal of Data Science and Machine Learning, 5(02), 41-55. https://doi.org/10.55640/ijdsml-05-02-04
B.-X. Wang, J.-L. Chen, and C.-L. Yu, ‘‘An AI-
powered networkthreat detection system,’’ IEEE Access, vol. 10, pp. 54029–54037,2022.
Benjamin G. Edelman, James Bono, Sida Peng, Roberto Rodriguez, and Sandra Ho. Randomized controlled trials for microsoft copilot for security. SSRN, March 2024.
Brendon McConnell. What’s logs got to do with it: On the perils of log dependent variables and difference-indifferences. arXiv preprint arXiv: 2308.00167, 2023.
C. Park, J. Lee, Y. Kim, J.-G. Park, H. Kim, and D. Hong, ‘‘An enhancedAI-based network intrusion detection system using generative adversar-ial networks,’’ IEEE Internet Things J., vol. 10, no. 3,
pp. 2330–2345,Feb. 2023.
D. Javeed, T. Gao, P. Kumar, and A. Jolfaei, ‘‘An explainable and resilientintrusion detection system for Industry 5.0,’’ IEEE Trans. Consum. Elec-tron., vol. 70, no. 1, pp. 1342– 1350, Jun. 2023.
Dona Ghosh, Rajarshi Ghosh, Sahana Roy Chowdhury, and Boudhayan Ganguly. Ai-exposure and labour market: a systematic literature review on estimations, validations, and perceptions. Management Review Quarterly, 70(1):1644–1658, 2024.
Krishna, K. (2022). Optimizing query performance in distributed NoSQL databases through adaptive indexing and data partitioning techniques. International Journal of Creative Research Thoughts.
Krishna, K., & Thakur, D. (2021). Automated machine learning (AutoML) for real-time data streams: Challenges and innovations in online learning algorithms. Journal of Emerging Technologies and Innovative Research, 8(12).
Mehra, A. (2021). Uncertainty quantification in deep neural networks: Techniques and applications in autonomous decision-making systems. World Journal of Advanced Research and Reviews, 11(3), 482–490.
Simran, S. Kumar, and A. Hans, ‘‘The AI shield and red AI framework:Machine learning solutions
for cyber threat intelligence(CTI),’’ in Proc.Int. Conf. Intell. Syst. Cybersecurity (ISCS), May 2024,
pp. 1–6.1
Thakur, D. (2020). Optimizing query performance in distributed databases using machine2 learning techniques: A comprehensive analysis and implementation. Iconic Research and Engineering Journals, 3, 12.
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Copyright (c) 2025 Tang Shu Qi (Author)
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