The concept of Cyber-Physical Systems(CPS)enables the creation of a complex network that includes sensors integrated into vehicles and infrastructure,facilitating seamless data acquisition and transfer.This review exa...The concept of Cyber-Physical Systems(CPS)enables the creation of a complex network that includes sensors integrated into vehicles and infrastructure,facilitating seamless data acquisition and transfer.This review examines the convergence of CPS and Industry 4.0 in the smart transportation sector,highlighting their transformative impact on Intelligent Transportation Systems(ITS)operations.It explores the integration of Industry 4.0 and CPS technologies in intelligent transportation,highlighting their roles in enhancing efficiency,safety,and sustainability.A systematic framework is proposed for developing,implementing,and managing these technologies in the transportation industry.Moreover,the review discusses frequent obstacles during technology integration in transportation and presents future research trends and innovations in intelligent transportation operations post-Industry 4.0 and CPS integration.Lastly,it emphasizes the critical need for standardized protocols and encryption methodologies to enhance the security of communication and data exchange among CPS components in transportation infrastructure.展开更多
The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection(FDI)attacks,posing critical threats to operational integrity,resource manag...The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection(FDI)attacks,posing critical threats to operational integrity,resource management,and public safety.Traditional detection mechanisms often struggle to generalize across heterogeneous environments or adapt to sophisticated,stealthy threats.To address these challenges,we propose a novel evolutionary optimized transformer-based deep reinforcement learning framework(Evo-Transformer-DRL)designed for robust and adaptive FDI detection in smart water infrastructures.The proposed architecture integrates three powerful paradigms:a transformer encoder for modeling complex temporal dependencies in multivariate time series,a DRL agent for learning optimal decision policies in dynamic environments,and an evolutionary optimizer to fine-tune model hyper-parameters.This synergy enhances detection performance while maintaining adaptability across varying data distributions.Specifically,hyper-parameters of both the transformer and DRL modules are optimized using an improved grey wolf optimizer(IGWO),ensuring a balanced trade-off between detection accuracy and computational efficiency.The model is trained and evaluated on three realistic Industry 4.0 water datasets:secure water treatment(SWaT),water distribution(WADI),and battle of the attack detection algorithms(BATADAL),which capture diverse attack scenarios in smart treatment and distribution systems.Comparative analysis against state-of-the-art baselines including Transformer,DRL,bidirectional encoder representations from transformers(BERT),convolutional neural network(CNN),long short-term memory(LSTM),and support vector machines(SVM)demonstrates that our proposed Evo-Transformer-DRL framework consistently outperforms others in key metrics such as accuracy,recall,area under the curve(AUC),and execution time.Notably,it achieves a maximum detection accuracy of 99.19%,highlighting its strong generalization capability across different testbeds.These results confirm the suitability of our hybrid framework for real-world Industry 4.0 deployment,where rapid adaptation,scalability,and reliability are paramount for securing critical infrastructure systems.展开更多
文摘The concept of Cyber-Physical Systems(CPS)enables the creation of a complex network that includes sensors integrated into vehicles and infrastructure,facilitating seamless data acquisition and transfer.This review examines the convergence of CPS and Industry 4.0 in the smart transportation sector,highlighting their transformative impact on Intelligent Transportation Systems(ITS)operations.It explores the integration of Industry 4.0 and CPS technologies in intelligent transportation,highlighting their roles in enhancing efficiency,safety,and sustainability.A systematic framework is proposed for developing,implementing,and managing these technologies in the transportation industry.Moreover,the review discusses frequent obstacles during technology integration in transportation and presents future research trends and innovations in intelligent transportation operations post-Industry 4.0 and CPS integration.Lastly,it emphasizes the critical need for standardized protocols and encryption methodologies to enhance the security of communication and data exchange among CPS components in transportation infrastructure.
文摘The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection(FDI)attacks,posing critical threats to operational integrity,resource management,and public safety.Traditional detection mechanisms often struggle to generalize across heterogeneous environments or adapt to sophisticated,stealthy threats.To address these challenges,we propose a novel evolutionary optimized transformer-based deep reinforcement learning framework(Evo-Transformer-DRL)designed for robust and adaptive FDI detection in smart water infrastructures.The proposed architecture integrates three powerful paradigms:a transformer encoder for modeling complex temporal dependencies in multivariate time series,a DRL agent for learning optimal decision policies in dynamic environments,and an evolutionary optimizer to fine-tune model hyper-parameters.This synergy enhances detection performance while maintaining adaptability across varying data distributions.Specifically,hyper-parameters of both the transformer and DRL modules are optimized using an improved grey wolf optimizer(IGWO),ensuring a balanced trade-off between detection accuracy and computational efficiency.The model is trained and evaluated on three realistic Industry 4.0 water datasets:secure water treatment(SWaT),water distribution(WADI),and battle of the attack detection algorithms(BATADAL),which capture diverse attack scenarios in smart treatment and distribution systems.Comparative analysis against state-of-the-art baselines including Transformer,DRL,bidirectional encoder representations from transformers(BERT),convolutional neural network(CNN),long short-term memory(LSTM),and support vector machines(SVM)demonstrates that our proposed Evo-Transformer-DRL framework consistently outperforms others in key metrics such as accuracy,recall,area under the curve(AUC),and execution time.Notably,it achieves a maximum detection accuracy of 99.19%,highlighting its strong generalization capability across different testbeds.These results confirm the suitability of our hybrid framework for real-world Industry 4.0 deployment,where rapid adaptation,scalability,and reliability are paramount for securing critical infrastructure systems.
