In this paper,we explore the ever-changing field ofDigital Twins(DT)in the Industrial Internet of Things(IIoT)context,emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0.The ...In this paper,we explore the ever-changing field ofDigital Twins(DT)in the Industrial Internet of Things(IIoT)context,emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0.The article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT,focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real integration.It discusses the emergence of DT,contextualizing its evolution within the framework of IIoT.The study categorizes the different types of DT,including prototypes and instances,and provides an in-depth analysis of the enabling technologies such as IoT,Artificial Intelligence(AI),Extended Reality(XR),cloud computing,and the Application Programming Interface(API).The paper demonstrates theDT advantages through the practical integration of real-world case studies,which highlights the technology’s exceptional capacity to improve traceability and fault detection within the context of the IIoT.This paper offers a focused,application-driven perspective on DTs in IIoT,specifically highlighting their role in key production phases such as designing,intelligent manufacturing,maintenance,resource management,automation,security,and safety.By emphasizing their potential to support human-centric,sustainable advancements in Industry 5.0,this study distinguishes itself from existing literature.It provides valuable insights that connect theoretical advancements with practical implementation,making it a crucial resource for researchers,practitioners,and industry professionals.展开更多
The byte stream is widely used in malware detection due to its independence of reverse engineering.However,existing methods based on the byte stream implement an indiscriminate feature extraction strategy,which ignore...The byte stream is widely used in malware detection due to its independence of reverse engineering.However,existing methods based on the byte stream implement an indiscriminate feature extraction strategy,which ignores the byte function difference in different segments and fails to achieve targeted feature extraction for various byte semantic representation modes,resulting in byte semantic confusion.To address this issue,an enhanced adversarial byte function associated method for malware backdoor attack is proposed in this paper by categorizing various function bytes into three functions involving structure,code,and data.The Minhash algorithm,grayscale mapping,and state transition probability statistics are then used to capture byte semantics from the perspectives of text signature,spatial structure,and statistical aspects,respectively,to increase the accuracy of byte semantic representation.Finally,the three-channel malware feature image is constructed based on different function byte semantics,and a convolutional neural network is applied for detection.Experiments on multiple data sets from 2018 to 2021 show that the method can effectively combine byte functions to achieve targeted feature extraction,avoid byte semantic confusion,and improve the accuracy of malware detection.展开更多
基金funded by Big Data Analytics Centre(BIDAC)of United Arab Emirates University under the grant numbers G00003679 and G00004526。
文摘In this paper,we explore the ever-changing field ofDigital Twins(DT)in the Industrial Internet of Things(IIoT)context,emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0.The article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT,focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real integration.It discusses the emergence of DT,contextualizing its evolution within the framework of IIoT.The study categorizes the different types of DT,including prototypes and instances,and provides an in-depth analysis of the enabling technologies such as IoT,Artificial Intelligence(AI),Extended Reality(XR),cloud computing,and the Application Programming Interface(API).The paper demonstrates theDT advantages through the practical integration of real-world case studies,which highlights the technology’s exceptional capacity to improve traceability and fault detection within the context of the IIoT.This paper offers a focused,application-driven perspective on DTs in IIoT,specifically highlighting their role in key production phases such as designing,intelligent manufacturing,maintenance,resource management,automation,security,and safety.By emphasizing their potential to support human-centric,sustainable advancements in Industry 5.0,this study distinguishes itself from existing literature.It provides valuable insights that connect theoretical advancements with practical implementation,making it a crucial resource for researchers,practitioners,and industry professionals.
基金This work is supported in part by the Information Security Software Project(2020)of the Ministry of Industry and Information Technology,PR China under Grant CEIEC-2020-ZM02-0134.
文摘The byte stream is widely used in malware detection due to its independence of reverse engineering.However,existing methods based on the byte stream implement an indiscriminate feature extraction strategy,which ignores the byte function difference in different segments and fails to achieve targeted feature extraction for various byte semantic representation modes,resulting in byte semantic confusion.To address this issue,an enhanced adversarial byte function associated method for malware backdoor attack is proposed in this paper by categorizing various function bytes into three functions involving structure,code,and data.The Minhash algorithm,grayscale mapping,and state transition probability statistics are then used to capture byte semantics from the perspectives of text signature,spatial structure,and statistical aspects,respectively,to increase the accuracy of byte semantic representation.Finally,the three-channel malware feature image is constructed based on different function byte semantics,and a convolutional neural network is applied for detection.Experiments on multiple data sets from 2018 to 2021 show that the method can effectively combine byte functions to achieve targeted feature extraction,avoid byte semantic confusion,and improve the accuracy of malware detection.
