Predictive Cyber Security Ecosystem Based on Federated Digital Twins Using Generative Artificial Intelligence (AI).

The fast-growing interconnection of digitized infrastructures and increasing sophistication of cyber-attacks have created unprecedented challenges for modern cybersecurity systems. Innovative technologies like Artificial Intelligence (AI), Digital Twins, and Federated Learning offer new avenues for developing smart, adaptive, and resilient cybersecurity solutions, which can deal with the emerging threats. However, conventional cybersecurity systems are based on the use of centralized architecture, reactive approaches to threat detection, and static security concepts. Moreover, contemporary security solutions relying on artificial intelligence experience various limitations concerning the protection of personal information, threat intelligence sharing among organizations, and creating realistic cyber-attacks for simulation. Therefore, there is an acute need for designing a scalable and secure cybersecurity ecosystem that can predict potential threats and help implement autonomous defensive measures. It is crucial to address the existing limitations and create a solution capable of increasing cybersecurity resilience. This paper presents the development of the Federated Digital Twin-Based Cybersecurity Ecosystem Using Generative AI for Predictive Attack Simulation and Defense. This framework incorporates digital twin technology in order to model the changing cyber-environment, utilizes federated learning to facilitate distributed threat intelligence sharing and employs Generative AI for effective attack simulation and defense generation.

Cybersecurity Predictive Ecosystem, Digital Twin Federated System, Generative AI, Federated Learning, Autonomous Cybersecurity

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