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.

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.

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.

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 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.

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.

Scheme on Cross-Domain Identity Authentication Based on Group Signature for Cloud Computing

In the cloud computing, different cloud service providers are often in different trust domains. As the traditional identity authentication mode cannot be applied to the cloud computing, the cross-domain identity authentication mechanism is needed to solve the identity authentication problem in the cloud computing. In view of the security problems in cloud computing, a cross-domain identity authentication scheme based on group signature is proposed. This scheme introduces a group of cloud service providers and users who are located in different trust domains. Any member of the group can generate the signature on behalf of the whole group, making the user access the cloud service provider in the case of privacy security. At the same time, with traceability it can track illegal operation of illegal users. In addition, the scheme uses the Chinese Remainder Theorem to integrate the message, and it can control the length of the data in the calculation process, simplifying the calculation process. It also realizes the join and revocation of group members without changing the key of other legitimate group members, and the maintenance cost of authentication schemes is low. The results show that the scheme has the advantages of anonymity, anti-counterfeit, traceability, anti-joint attack and so on. It can not only realize tracking function under the condition of guaranteeing user's privacy, but can also simplify the authentication calculation process to improve the efficiency of the cross domain identity authentication, and its performance is more suitable for large-scale cloud computing environment.

异构无人系统协同控制研究进展

面对复杂化、多样化和立体化的任务需求,异构无人系统将空、地、海跨域优势互补,最大化提升系统各方面能力,为未来协同作战和智慧民生提供有力的技术保障.首先,梳理了国内外在跨域协同领域出台的推动性文件,介绍了异构无人系统在军用、科研院所和民用等三方面的实际应用.其次,针对异构无人系统关键技术之一的协同控制,分别阐述了异构无人系统协同控制在一致性、轨迹跟踪和编队‒合围等三方面的最新进展.此外,还特别讨论了在不同通信条件下协同控制所面临的挑战和解决方案.其中,由于一致性和轨迹跟踪存在上层控制理论性和下层控制可行性的区别,进一步从滑模控制、自适应控制、反步控制和自适应动态规划等四方面对轨迹跟踪研究展开阐述.然后,为了更好地促进异构无人系统协同控制在实际中的应用,通过总结前人相关工作,讨论了其在多约束控制与实时性、多任务切换控制和跨域通信下稳定性等三方面亟需解决的技术瓶颈.最后,综合异构无人系统协同控制研究现状和实际需求来看,指出了其在深度强化学习与分布式博弈、人机交互以及反群体智能等三方面的发展趋势.

Words alignment based on association rules for cross-domain sentiment classification

Automatic classification of sentiment data（e.g., reviews, blogs） has many applications in enterprise user management systems, and can help us understand people＇s attitudes about products or services. However, it is difficult to train an accurate sentiment classifier for different domains. One of the major reasons is that people often use different words to express the same sentiment in different domains, and we cannot easily find a direct mapping relationship between them to reduce the differences between domains. So, the accuracy of the sentiment classifier will decline sharply when we apply a classifier trained in one domain to other domains. In this paper, we propose a novel approach called words alignment based on association rules（WAAR） for cross-domain sentiment classification,which can establish an indirect mapping relationship between domain-specific words in different domains by learning the strong association rules between domain-shared words and domain-specific words in the same domain. In this way, the differences between the source domain and target domain can be reduced to some extent, and a more accurate cross-domain classifier can be trained. Experimental results on Amazon~ datasets show the effectiveness of our approach on improving the performance of cross-domain sentiment classification.

Scalable protocol for cross-domain group password-based authenticated key exchange

Cross-domain password-based authenticated key exchange （PAKE） protocols have been studied for many years. However, these protocols are mainly focusing on multi-participant within a single domain in an open network environment. This paper proposes a novel approach for designing a cross-domain group PAKE protocol, that primarily handles with the setting of multi-participant in the multi- domain. Moreover, our protocol is proved secure against active adversary in the Real-or-Random （ROR） model. In our protocol, no interaction occurs between any two domain authentication servers. They are regarded as ephemeral certificate authorities （CAs） to certify key materials that participants might subsequently use to exchange and agree on group session key. We further justify the computational complexity and measure the average computation time of our protocol. To the best of our knowledge, this is the first work to analyze and discuss a provably secure multi-participant cross-domain group PAKE protocol.

Weighted Co-Training for Cross-Domain Image SentimentClassification

Image sentiment classification, which aims to predict the polarities of sentiments conveyed by the images, has gained a lot of attention. Most existing methods address this problem by training a general classifier with certain visual features, ignoring the discrepancies across domains. In this paper, we propose a novel weighted co-training method for cross-domain image sentiment classification, which iteratively enlarges the labeled set by introducing new high-confidence classified samples to reduce the gap between the two domains. We train two sentiment classifiers with both the images and the corresponding textual comments separately, and set the similarity between the source domain and the target domain as the weight of a classifier. We perform extensive experiments on a real Flickr dataset to evaluate the proposed method, and the empirical study reveals that the weighted co-training method significantly outperforms some baseline solutions.

ATLRec:An Attentional Adversarial Transfer Learning Network for Cross-Domain Recommendation

Entity linking is a new technique in recommender systems to link users'interaction behaviors in different domains,for the purpose of improving the performance of the recommendation task.Linking-based cross-domain recom-mendation aims to alleviate the data sparse problem by utilizing the domain-sharable knowledge from auxiliary domains.However,existing methods fail to prevent domain-specific features to be transferred,resulting in suboptimal results.In this paper,we aim to address this issue by proposing an adversarial transfer learning based model ATLRec,which effec-tively captures domain-sharable features for cross-domain recommendation.In ATLRec,we leverage adversarial learning to generate representations of user-item interactions in both the source and the target domains,such that the discrimina-tor cannot identify which domain they belong to,for the purpose of obtaining domain-sharable features.Meanwhile each domain learns its domain-specific features by a private feature extractor.The recommendation of each domain considers both domain-specific and domain-sharable features.We further adopt an attention mechanism to learn item latent factors of both domains by utilizing the shared users with interaction history,so that the representations of all items can be learned sufficiently in a shared space,even when few or even no items are shared by different domains.By this method,we can represent all items from the source and the target domains in a shared space,for the purpose of better linking items in different domains and capturing cross-domain item-item relatedness to facilitate the learning of domain-sharable knowledge.The proposed model is evaluated on various real-world datasets and demonstrated to outperform several state-of-the-art single-domain and cross-domain recommendation methods in terms of recommendation accuracy.

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.