• A new concept and framework of the Resilient Cognitive Twin (RCT). • A new generative approach with three enabling mechanisms. • An evaluation case study of a city flooding resilience system. Improving the resilience of an engineering system can be addressed from both a physical engineered system (PES) and its virtual replica--digital twin (DT) system. On the one hand, a DT can enhance the resilience of its PES by enabling real-time monitoring/proactive intervention and predictive maintenance. On the other hand, its deep integration with PES also increases the complexity of the overall system--both virtual and physical. This interdependence can transform single-sided risks into overall system-wide vulnerabilities, potentially undermining--even compromising--the resilience of the PES itself. To address this challenge, this paper proposes a new concept and framework of the Resilient Cognitive Twin (RCT), that supports generative reconfiguration and bidirectional feedback across the human–cyber–physical continuum. The framework is supported by a generative approach with three new enabling mechanisms: (1) a requirement decomposition and recompositing mechanism with edge-cloud-centre collaboration for forming a unified yet loosely coupled and low-level foundation of RCT; (2) a dynamic evolution mechanism enabling right-time co-evolution of data-model-service relationships for a changing environment and stakeholder needs; and (3) a generative cognitive mechanism for situational awareness and decision-making, responding to changing situations with proper and resilient services and their coordination/scheduling. The proposed framework and enabling approach are validated through an urban flood resilient DT system, demonstrating its capability to enhance resilience in complex, distributed environments, paving the way for Human-Cyber-Physical Systems and future industrial and societal applications.

With the enlargement of wireless technology, Internet of Things (IoT) is emerging as a promising approach to realize smart cities and address lots of serious problems such as safety, convenience and efficiency. In order to avoid any possible rancorous attacks, employing lightweight cryptosystems is most effective to implement encryption/decryption, message authentication and digital signature for security of the IoT. LED is such a lightweight cipher with two flexible keysize variants in the IoT. Since its designing, a multitude of fault analysis techniques in chosen plaintext attacks focus on provoking faults on LED to derive the 64-bit and 128-bit secret keys. It is vital to investigate whether injecting faults allows breaking LED while the attackers have the weakest ciphertext-only attacking ability. This study presents ciphertext-only fault analysis with six different distinguishers on LED. The simulating experiments show that our analysis can recover its 64-bit and 128-bit secret keys with over 99 percent probability using the SEI, GF, GF-SEI, ML, HW and MAP distinguishers. The attack can not only improve the attacking efficiency, but also decrease the number of faults. The fault locations can be injected into the deeper round. It provides vital reference for security analysis of other lightweight ciphers in the IoT.