Zero trust architecture(ZTA)is a paradigm shift in how we protect data,stay connected and access resources.ZTA is non-perimeter-based defence,which has been emerging as a promising revolution in the cyber security fie...Zero trust architecture(ZTA)is a paradigm shift in how we protect data,stay connected and access resources.ZTA is non-perimeter-based defence,which has been emerging as a promising revolution in the cyber security field.It can be used to continuously maintain security by safeguarding against attacks both from inside and outside of the network system.However,ZTA automation and orchestration,towards seamless deployment on real-world networks,has been limited to be reviewed in the existing literature.In this paper,we first identify the bottlenecks,discuss the background of ZTA and compare it with traditional perimeter-based security architectures.More importantly,we provide an in-depth analysis of state-of-the-art AI techniques that have the potential in the automation and orchestration of ZTA.Overall,in this review paper,we develop a foundational view on the challenges and potential enablers for the automation and orchestration of ZTA.展开更多
Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model′s interpretability and accuracy.This paper introduces an end-to-end d...Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model′s interpretability and accuracy.This paper introduces an end-to-end deep learning model,named representation-enhanced knowledge graph convolutional networks(RKGCN),which dynamically analyses each user′s preferences and makes a recommendation of suitable items.It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information in knowledge graphs.RKGCN is able to offer more personalized and relevant recommendations in three different scenarios.The experimental results show the superior effectiveness of our model over 5 baseline models on three real-world datasets including movies,books,and music.展开更多
文摘Zero trust architecture(ZTA)is a paradigm shift in how we protect data,stay connected and access resources.ZTA is non-perimeter-based defence,which has been emerging as a promising revolution in the cyber security field.It can be used to continuously maintain security by safeguarding against attacks both from inside and outside of the network system.However,ZTA automation and orchestration,towards seamless deployment on real-world networks,has been limited to be reviewed in the existing literature.In this paper,we first identify the bottlenecks,discuss the background of ZTA and compare it with traditional perimeter-based security architectures.More importantly,we provide an in-depth analysis of state-of-the-art AI techniques that have the potential in the automation and orchestration of ZTA.Overall,in this review paper,we develop a foundational view on the challenges and potential enablers for the automation and orchestration of ZTA.
文摘Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model′s interpretability and accuracy.This paper introduces an end-to-end deep learning model,named representation-enhanced knowledge graph convolutional networks(RKGCN),which dynamically analyses each user′s preferences and makes a recommendation of suitable items.It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information in knowledge graphs.RKGCN is able to offer more personalized and relevant recommendations in three different scenarios.The experimental results show the superior effectiveness of our model over 5 baseline models on three real-world datasets including movies,books,and music.
