Blockchain-Based Certificateless Cross-Domain Authentication Scheme in the Industrial Internet of Things

The Industrial Internet of Things(IIoT)consists of massive devices in different management domains,and the lack of trust among cross-domain entities leads to risks of data security and privacy leakage during information exchange.To address the above challenges,a viable solution that combines Certificateless Public Key Cryptography(CL-PKC)with blockchain technology can be utilized.However,as many existing schemes rely on a single Key Generation Center(KGC),they are prone to problems such as single points of failure and high computational overhead.In this case,this paper proposes a novel blockchain-based certificateless cross-domain authentication scheme,that integrates the threshold secret sharing mechanism without a trusted center,meanwhile,adopts blockchain technology to enable cross-domain entities to authenticate with each other and to negotiate session keys securely.This scheme also supports the dynamic joining and removing of multiple KGCs,ensuring secure and efficient cross-domain authentication and key negotiation.Comparative analysiswith other protocols demonstrates that the proposed cross-domain authentication protocol can achieve high security with relatively lowcomputational overhead.Moreover,this paper evaluates the scheme based on Hyperledger Fabric blockchain environment and simulates the performance of the certificateless scheme under different threshold parameters,and the simulation results show that the scheme has high performance.

A Blockchain-Based Efficient Cross-Domain Authentication Scheme for Internet of Vehicles

The Internet of Vehicles(IoV)is extensively deployed in outdoor and open environments to effectively address traffic efficiency and safety issues by connecting vehicles to the network.However,due to the open and variable nature of its network topology,vehicles frequently engage in cross-domain interactions.During such processes,directly uploading sensitive information to roadside units for interaction may expose it to malicious tampering or interception by attackers,thus compromising the security of the cross-domain authentication process.Additionally,IoV imposes high real-time requirements,and existing cross-domain authentication schemes for IoV often encounter efficiency issues.To mitigate these challenges,we propose CAIoV,a blockchain-based efficient cross-domain authentication scheme for IoV.This scheme comprehensively integrates technologies such as zero-knowledge proofs,smart contracts,and Merkle hash tree structures.It divides the cross-domain process into anonymous cross-domain authentication and safe cross-domain authentication phases to ensure efficiency while maintaining a balance between efficiency and security.Finally,we evaluate the performance of CAIoV.Experimental results demonstrate that our proposed scheme reduces computational overhead by approximately 20%,communication overhead by around 10%,and storage overhead by nearly 30%.

A Post-Quantum Cross-Domain Authentication Scheme Based on Multi-Chain Architecture

Due to the rapid advancements in network technology,blockchain is being employed for distributed data storage.In the Internet of Things(IoT)scenario,different participants manage multiple blockchains located in different trust domains,which has resulted in the extensive development of cross-domain authentication techniques.However,the emergence of many attackers equipped with quantum computers has the potential to launch quantum computing attacks against cross-domain authentication schemes based on traditional cryptography,posing a significant security threat.In response to the aforementioned challenges,our paper demonstrates a post-quantum cross-domain identity authentication scheme to negotiate the session key used in the cross-chain asset exchange process.Firstly,our paper designs the hiding and recovery process of user identity index based on lattice cryptography and introduces the identity-based signature from lattice to construct a post-quantum cross-domain authentication scheme.Secondly,our paper utilizes the hashed time-locked contract to achieves the cross-chain asset exchange of blockchain nodes in different trust domains.Furthermore,the security analysis reduces the security of the identity index and signature to Learning With Errors(LWE)and Short Integer Solution(SIS)assumption,respectively,indicating that our scheme has post-quantum security.Last but not least,through comparison analysis,we display that our scheme is efficient compared with the cross-domain authentication scheme based on traditional cryptography.

Cross-Domain Authentication Scheme Based on Blockchain and Consistent Hash Algorithm for System-Wide Information Management

System-wide information management(SWIM)is a complex distributed information transfer and sharing system for the next generation of Air Transportation System(ATS).In response to the growing volume of civil aviation air operations,users accessing different authentication domains in the SWIM system have problems with the validity,security,and privacy of SWIM-shared data.In order to solve these problems,this paper proposes a SWIM crossdomain authentication scheme based on a consistent hashing algorithm on consortium blockchain and designs a blockchain certificate format for SWIM cross-domain authentication.The scheme uses a consistent hash algorithm with virtual nodes in combination with a cluster of authentication centers in the SWIM consortium blockchain architecture to synchronize the user’s authentication mapping relationships between authentication domains.The virtual authentication nodes are mapped separately using different services provided by SWIM to guarantee the partitioning of the consistent hash ring on the consortium blockchain.According to the dynamic change of user’s authentication requests,the nodes of virtual service authentication can be added and deleted to realize the dynamic load balancing of cross-domain authentication of different services.Security analysis shows that this protocol can resist network attacks such as man-in-the-middle attacks,replay attacks,and Sybil attacks.Experiments show that this scheme can reduce the redundant authentication operations of identity information and solve the problems of traditional cross-domain authentication with single-point collapse,difficulty in expansion,and uneven load.At the same time,it has better security of information storage and can realize the cross-domain authentication requirements of SWIM users with low communication costs and system overhead.KEYWORDS System-wide information management(SWIM);consortium blockchain;consistent hash;cross-domain authentication;load balancing.

Cross-Domain Data Traceability Mechanism Based on Blockchain

With the application and development of blockchain technology,many problems faced by blockchain traceability are gradually exposed.Such as cross-chain information collaboration,data separation and storage,multisystem,multi-security domain collaboration,etc.To solve these problems,it is proposed to construct trust domains based on federated chains.The public chain is used as the authorization chain to build a cross-domain data traceability mechanism applicable to multi-domain collaboration.First,the architecture of the blockchain cross-domain model is designed.Combined with the data access strategy and the decision mechanism,the open and transparent judgment of cross-domain permission and cross-domain identity authentication is realized.And the public chain consensus node election mechanism is realized based on PageRank.Then,according to the characteristics of a nonsingle chain structure in the process of data flow,a data retrievalmechanism based on a Bloom filter is designed,and the cross-domain traceability algorithm is given.Finally,the safety and effectiveness of the traceability mechanism are verified by security evaluation and performance analysis.

BLECA: A Blockchain-Based Lightweight and Efficient Cross-Domain Authentication Scheme for Smart Parks

Smart parks serve as integral components of smart cities,where they play a pivotal role in the process of urban modernization.The demand for cross-domain cooperation among smart devices from various parks has witnessed a significant increase.To ensure secure communication,device identities must undergo authentication.The existing cross-domain authentication schemes face issues such as complex authentication paths and high certificate management costs for devices,making it impractical for resource-constrained devices.This paper proposes a blockchain-based lightweight and efficient cross-domain authentication protocol for smart parks,which simplifies the authentication interaction and requires every device to maintain only one certificate.To enhance cross-domain cooperation flexibility,a comprehensive certificate revocation mechanism is presented,significantly reducing certificate management costs while ensuring efficient and secure identity authentication.When a park needs to revoke access permissions of several cooperative partners,the revocation of numerous cross-domain certificates can be accomplished with a single blockchain write operation.The security analysis and experimental results demonstrate the security and effectiveness of our scheme.

A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

Smart city refers to the information system with Intemet of things and cloud computing as the core tec hnology and government management and industrial development as the core content,forming a large scale,heterogeneous and dynamic distributed Internet of things environment between different Internet of things.There is a wide demand for cooperation between equipment and management institutions in the smart city.Therefore,it is necessary to establish a trust mechanism to promote cooperation,and based on this,prevent data disorder caused by the interaction between honest terminals and malicious temminals.However,most of the existing research on trust mechanism is divorced from the Internet of things environment,and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of hings devices,resuling in the fact that the research on abstract trust trust mechanism cannot be directly applied to the Internet of things;On the other hand,various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered.Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals,a cross domain trust model(CDTM)based on self-authentication is proposed.Unlike most trust models,this model uses self-certified trust.The cross-domain process of internet of things(IoT)terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction.At the same time,in order to alleviate the collision attack and improve the accuracy of trust evaluation,the overall trust value is calculated by comprehensively considering the quantity weight,time attenuation weight and similarity weight.Finally,the simulation results show that CDTM has good anti collusion attack ability.The success rate of malicious interaction will not increase significantly.Compared with other models,the resource consumption of our proposed model is significantly reduced.

SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer

This study proposes a novel general image fusion framework based on cross-domain long-range learning and Swin Transformer,termed as SwinFusion.On the one hand,an attention-guided cross-domain module is devised to achieve sufficient integration of complementary information and global interaction.More specifically,the proposed method involves an intra-domain fusion unit based on self-attention and an interdomain fusion unit based on cross-attention,which mine and integrate long dependencies within the same domain and across domains.Through long-range dependency modeling,the network is able to fully implement domain-specific information extraction and cross-domain complementary information integration as well as maintaining the appropriate apparent intensity from a global perspective.In particular,we introduce the shifted windows mechanism into the self-attention and cross-attention,which allows our model to receive images with arbitrary sizes.On the other hand,the multi-scene image fusion problems are generalized to a unified framework with structure maintenance,detail preservation,and proper intensity control.Moreover,an elaborate loss function,consisting of SSIM loss,texture loss,and intensity loss,drives the network to preserve abundant texture details and structural information,as well as presenting optimal apparent intensity.Extensive experiments on both multi-modal image fusion and digital photography image fusion demonstrate the superiority of our SwinFusion compared to the state-of-theart unified image fusion algorithms and task-specific alternatives.Implementation code and pre-trained weights can be accessed at https://github.com/Linfeng-Tang/SwinFusion.

Context Information and Fragments Based Cross-Domain Word Segmentation

A new joint decoding strategy that combines the character-based and word-based conditional random field model is proposed.In this segmentation framework,fragments are used to generate candidate Out-of-Vocabularies(OOVs).After the initial segmentation,the segmentation fragments are divided into two classes as "combination"(combining several fragments as an unknown word) and "segregation"(segregating to some words).So,more OOVs can be recalled.Moreover,for the characteristics of the cross-domain segmentation,context information is reasonably used to guide Chinese Word Segmentation(CWS).This method is proved to be effective through several experiments on the test data from Sighan Bakeoffs 2007 and Bakeoffs 2010.The rates of OOV recall obtain better performance and the overall segmentation performances achieve a good effect.

FedCDR:Privacy-preserving federated cross-domain recommendation

Cross-Domain Recommendation(CDR)aims to solve data sparsity and cold-start problems by utilizing a relatively information-rich source domain to improve the recommendation performance of the data-sparse target domain.However,most existing approaches rely on the assumption of centralized storage of user data,which undoubtedly poses a significant risk of user privacy leakage because user data are highly privacy-sensitive.To this end,we propose a privacy-preserving Federated framework for Cross-Domain Recommendation,called FedCDR.In our method,to avoid leakage of user privacy,a general recommendation model is trained on each user's personal device to obtain embeddings of users and items,and each client uploads weights to the central server.The central server then aggregates the weights and distributes them to each client for updating.Furthermore,because the weights implicitly contain private information about the user,local differential privacy is adopted for the gradients before uploading them to the server for better protection of user privacy.To distill the relationship of user embedding between two domains,an embedding transformation mechanism is used on the server side to learn the cross-domain embedding transformation model.Extensive experiments on real-world datasets demonstrate that ourmethod achieves performance comparable with that of existing data-centralized methods and effectively protects user privacy.

Achieving Consistence for Cross-Domain WAN Control in Software-Defined Networks

When applying Software-Defined Networks(SDN) to WANs,the SDN flexibility enables the cross-domain control to achieve a better control scalability.However,the control consistence is required by all the cross-domain services,to ensure the data plane configured in consensus for different domains.Such consistence process is complicated by potential failure and errors of WANs.In this paper,we propose a consistence layer to actively and passively snapshot the cross-domain control states,to reduce the complexities of service realizations.We implement the layer and evaluate performance in the PlanetLab testbed for the WAN emulation.The testbed conditions are extremely enlarged comparing to the real network.The results show its scalability,reliability and responsiveness in dealing with the control dynamics.In the normalized results,the active and passive snapshots are executed with the mean times of 1.873 s and 105 ms in135 controllers,indicating its readiness to be used in the real network.

Exploration on the Frontiers of Flexible and Deformable Cross-Domain Intelligent Flight

As human aeronautic and aerospace technology continues to prosper and the aerial flight space domain further expands,traditional fixed-shape air vehicles have been confronted with difficulties in satisfying complex missions in cross-domain scenarios.Owing to their flexible and deformable appearance,morphing air vehicles are expected to realize cross-domain intelligent flight,thus emerging as the most subversive strategic development trend and research focus in aeronautic and aerospace fields.This paper primarily reviews the research background and challenges of flexible and deformable cross-domain intelligent flight,proposing a corresponding research framework and mode as well as exploring the scientific issues and state-of-the-art solutions,where key research progress is introduced.The explorations covered in this paper also provide ideas and directions for the study of deformable cross-domain intelligent flight,which has critical scientific significance in promoting the study itself.

Cross-domain diagnosis for polymer electrolyte membrane fuel cell based on digital twins and transfer learning network

Existing research on fault diagnosis for polymer electrolyte membrane fuel cells(PEMFC)has advanced significantly,yet performance is hindered by variations in data distributions and the requirement for extensive fault data.In this study,a cross-domain adaptive health diagnosis method for PEMFC is proposed,integrating the digital twin model and transfer convolutional diagnosis model.A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method.To extract long-term time series features from the data,a temporal convolutional network(TCN)is proposed as a pre-trained diagnosis model for the source domain,with feature extraction layers that can be reused to the transfer learning network.It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults,including pressure,drying,flow,and flooding faults,with 99.92%accuracy,through the effective capture of the long-term dependencies in time series data.Finally,a domain adaptive transfer convolutional network(DATCN)is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features.The results show that the DATCN model,tested on three different target domain devices with adversarial training using only 10%normal data,can achieve an average accuracy of 98.5%(30%improved over traditional diagnosis models).This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices,significantly reducing the reliance on extensive fault data.

CNN-Based Broad Learning for Cross-Domain Emotion Classification

Cross-domain emotion classification aims to leverage useful information in a source domain to help predict emotion polarity in a target domain in a unsupervised or semi-supervised manner.Due to the domain discrepancy,an emotion classifier trained on source domain may not work well on target domain.Many researchers have focused on traditional cross-domain sentiment classification,which is coarse-grained emotion classification.However,the problem of emotion classification for cross-domain is rarely involved.In this paper,we propose a method,called convolutional neural network(CNN)based broad learning,for cross-domain emotion classification by combining the strength of CNN and broad learning.We first utilized CNN to extract domain-invariant and domain-specific features simultaneously,so as to train two more efficient classifiers by employing broad learning.Then,to take advantage of these two classifiers,we designed a co-training model to boost together for them.Finally,we conducted comparative experiments on four datasets for verifying the effectiveness of our proposed method.The experimental results show that the proposed method can improve the performance of emotion classification more effectively than those baseline methods.

Cross-domain identity authentication scheme based on blockchain and PKI system

In vehicular ad hoc networks(VANET),the cross-domain identity authentication of users is very important for the development of VANET due to the large cross-domain mobility of vehicle users.The Public Key Infrastructure(PKI)system is often used to solve the identity authentication and security trust problems faced by VANET.However,the PKI system has challenges such as too centralized Authority of Certification Authority(CA),frequent cross-domain access to certificate interactions and high authentication volume,leading to high certificate management costs,complex cross-domain authentication paths,easy privacy leakage,and overburdened networks.To address these problems,this paper proposes a lightweight blockchain-based PKI identity management and authentication architecture that uses smart contracts to reduce the heavy burden caused by CAs directly managing the life cycle of digital certificates.On this basis,a trust chain based on smart contracts is designed to replace the traditional CA trust chain to meet the general cross-domain requirements,to effectively avoid the communication pressure caused by a mass of certificate transmissions.For the cross-domain scenario with higher privacy and security requirements the identity attribute authentication service is provided directly while protecting privacy by using the Merkle tree to anchor identity attribute data on and off the blockchain chain.Finally,the proposed scheme was comprehensively analyzed in terms of cost,time consumption and security.

Teachers cooperation:team-knowledge distillation for multiple cross-domain few-shot learning

Although few-shot learning(FSL)has achieved great progress,it is still an enormous challenge especially when the source and target set are from different domains,which is also known as cross-domain few-shot learning(CD-FSL).Utilizing more source domain data is an effective way to improve the performance of CD-FSL.However,knowledge from different source domains may entangle and confuse with each other,which hurts the performance on the target domain.Therefore,we propose team-knowledge distllation networks(TKD-Net)to tackle this problem,which explores a strategy to help the cooperation of multiple teachers.Specifically,we distill knowledge from the cooperation of teacher networks to a single student network in a meta-learning framework.It incorporates task-oriented knowledge distillation and multiple cooperation among teachers to train an efficient student with better generalization ability on unseen tasks.Moreover,our TKD-Net employs both response-based knowledge and relation-based knowledge to transfer more comprehensive and effective knowledge.Extensive experimental results on four fine-grained datasets have demonstrated the effectiveness and superiority of our proposed TKD-Net approach.

Three Heads Better than One:Pure Entity,Relation Label and Adversarial Training for Cross-domain Few-shot Relation Extraction

In this paper,we study cross-domain relation extraction.Since new data mapping to feature spaces always differs from the previously seen data due to a domain shif,few-shot relation extraction often perform poorly.To solve the problems caused by cross-domain,we propose a method for combining the pure entity,relation labels and adversarial(PERLA).We first use entities and complete sentences for separate encoding to obtain context-independent entity features.Then,we combine relation labels which are useful for relation extraction to mitigate context noise.We combine adversarial to reduce the noise caused by cross-domain.We conducted experiments on the publicly available cross-domain relation extraction dataset Fewrel 2.o[1]o,and the results show that our approach improves accuracy and has better transferability for better adaptation to cross-domain tasks.

跨域数据管理

随着数据成为新的生产要素和数字中国顶层战略的推进,跨域数据共享和流通对于实现数据要素价值最大化变得至关重要。国家通过布局全国一体化大数据中心体系、启动“东数西算”工程等一系列举措,为数据要素的跨域应用提供了基础设施。然而,传统的数据管理局限于单一域内,无法满足跨域场景下的数据管理需求。跨域数据管理面临通信层面的跨空间域挑战、数据建模层面的异构模型融合问题,以及数据访问层面的跨信任域挑战。从跨空间域、跨管辖域和跨信任域3个视角出发,探讨了跨域数据管理的内涵、研究挑战及关键技术,并展望了其未来发展趋势。

卫星互联网支撑跨域无人集群作战

跨域无人集群作为一种颠覆性的未来全新作战模式,其作战效能的发挥极大地受限于通信组网能力。近年来,卫星互联网凭借其广覆盖、低延时、高宽带及低成本等技术优势,在全球快速发展,是进一步发展跨域无人集群作战模式的关键。文中针对未来水下无人系统的发展趋势和现实需求,介绍了卫星互联网的发展、技术优势以及跨域无人集群的作战概念,深入分析了卫星互联网支撑跨域无人集群作战的优势,提出了结合卫星互联网技术与空域、海面、水下作战域无人集群相结合的空海跨域无人集群作战网络,并综合分析给出了技术架构,可为未来跨域无人集群作战研究提供参考。

成渝地区双城经济圈区域协调发展的理论逻辑、实践创新与优化路径

成渝地区双城经济圈建设是深入贯彻党的区域协调发展战略、扎实推进中国式现代化的重要实践途径。成渝地区历经从经济区建设到城市群发展、再到双城经济圈构建的区域协调发展实践进程,基于对跨域治理需求的现实考量,通过调整府际关系、强化地方政府间合作、构建横向行政协调机制,以突破行政区划限制、促进区域资源优化配置、实现风险共担和利益共享,从而推动经济社会、生态环境等层面的均衡发展。新时代十年成渝地区双城经济圈在合作领域、协调机制、利益共享、政策执行方面创新制度体系,实践混合共力型的区域协调发展模式。但是未来在区域规划、利益格局、组织协同、资源配置上成渝地区双城经济圈建设仍面临诸多阻碍因素,需要从理念重塑、外部支持与内部保障三个维度进行路径优化。推动成渝地区双城经济圈建设向高质量阶段迈进意义重大,进一步从规划层面、执行层面、效能层面探索制度措施和协作策略,为形成各具特色、优势互补的区域发展新格局提供实践经验和路径借鉴。