The Neuromorphic Cyber-Twin: A Conceptual Architecture for Cognitive Defense in Digital Twin Ecosystems

Nida Nasir^1*Hussam Al Hamadi²

• ¹Université Gustave Eiffel, Bouguenais, France
• ²University of Dubai, Dubai, United Arab Emirates

As cyber-physical systems become increasingly virtualized, digital twins are emerging as critical components for real-time system monitoring, simulation, and control. However, their growing complexity and exposure to dynamic network environments make them susceptible to sophisticated cyber threats. Traditional cybersecurity models, often rule-based or machine-learning-based, struggle to adapt in real time to evolving attack patterns, especially within decentralized and resource-constrained settings. In this conceptual paper, we introduce the Neuromorphic Cyber-Twin (NCT), a brain-inspired architectural framework that leverages spiking neural networks (SNNs) and event-driven cognition to endow digital twins with adaptive, self-evolving cyber-defense capabilities. The NCT framework is grounded in neuromorphic principles such as sparse coding, temporal encoding, and synaptic plasticity (e.g., spike-timing-dependent plasticity, STDP), enabling it to process telemetry data from the digital-twin layer as spike-based sensory input. We present a layered architecture in which the cognitive layer continuously monitors behavioral deviations, performs anomaly inference, and autonomously adapts its defense responses in alignment with system dynamics. This biologically inspired paradigm offers low-latency detection, contextual awareness, and energy efficiency, essential for scalable security in virtualized ecosystems. We discuss theoretical foundations, architecture components, and application scenarios in infrastructure, autonomous transport, and industrial control systems. All simulations and examples are provided as lightweight prototypes to illustrate feasibility, while comprehensive benchmarking and large-scale validation are reserved for future work.