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 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.

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 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.

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.

面向自杀式无人机饱和攻击的海空跨域无人协同反制策略

针对海洋环境下反制自杀式无人机饱和攻击问题,文中研究了目标数量远超我方情况下的海空跨域无人平台协同反制策略,提出一种结合改进的遗传算法与联盟形成博弈的协同算法。首先根据海空跨域无人平台攻击特性与运动特性,结合最大最小策略设计代价函数;然后结合任务需求对遗传算法进行改进,对交叉和变异过程进行引导和限制,在提升遗传算法效率的基础上生成可行的反制方案;最后设计联盟形成规则,通过联盟之间的成员变动使各联盟达到纳什稳定状态,在算子数量较多的情况下仍能使反制方案被持续稳定优化。仿真对比实验表明所提策略具有可行性和优越性,能在目标遭受饱和攻击时提供合理高效的反制方案,可为大规模跨域无人集群作战研究提供参考。

海空跨域协同兵棋AI架构设计及关键技术分析

以深度强化学习为核心的智能博弈技术在游戏领域内的突破和进展为海空兵棋AI的研究提供了借鉴。智能体架构设计是需要解决的关键问题,良好的架构能够降低算法训练的复杂度和难度,加快策略收敛。提出基于随机博弈的海空跨域协同决策博弈模型,分析了相关的均衡解概念;在分析典型智能体框架基础上,针对海空兵棋推演决策博弈过程,提出基于多智能体分层强化学习的智能体双层架构,能够有效解决智能体间协作和维度灾难问题;从兵力协同、智能体网络设计、对手建模和训练机制共4个方面分析了关键技术。期望为海空兵棋AI设计实现提供架构指导。

A Trust Evaluation Mechanism Based on Autoencoder Clustering Algorithm for Edge Device Access of IoT

First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism based on the time decay factor is proposed,taking into account the influence of historical interaction records.We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data.We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record.Then we propose an autoencoder-based trust clustering algorithm.We perform feature extraction based on autoencoders.Kullback leibler(KL)divergence is used to calculate the reconstruction error.When constructing a convolutional autoencoder,we introduce convolutional neural networks to improve training efficiency and introduce sparse constraints into the hidden layer of the autoencoder.The sparse penalty term in the loss function measures the difference through the KL divergence.Trust clustering is performed based on the density based spatial clustering of applications with noise(DBSCAN)clustering algorithm.During the clustering process,edge nodes have a variety of trustworthy attribute characteristics.We assign different attribute weights according to the relative importance of each attribute in the clustering process,and a larger weight means that the attribute occupies a greater weight in the calculation of distance.Finally,we introduced adaptive weights to calculate comprehensive trust evaluation.Simulation experiments prove that our trust evaluation mechanism has excellent reliability and accuracy.

智能无人集群系统跨域协同技术研究现状与展望

随着智能化技术和无人系统技术的快速发展,智能无人集群系统跨域协同的概念应运而生并得到了广泛关注与深入研究,智能无人集群系统跨域协同技术逐渐成为世界各国抢占无人系统技术竞争中的制高点。本文从我国智能无人集群系统跨域协同技术的发展需求出发,梳理了海空、空地以及海陆/海陆空等典型无人场景跨域协同技术的研究现状;深入分析了智能无人集群系统跨域协同技术的发展现状、技术需求以及未来的重点研究方向。最后,从总体思路、体系架构、理论创新和技术攻关4个层面,提出了推动智能无人集群系统跨域协同技术稳健与快速发展的对策建议,以期促进我国无人系统应用能力的持续提升。

The homotopic method of travelling wave solution for El Nio tropic sea-air coupled oscillator

The EI Nimo and Southern Oscillation （ENSO） is an interannual phenomenon involved in the tropical Pacific sea-air interactions. In this paper, an asymptotic method of solving nonlinear equations for the ENSO model is proposed. And based on a class of oscillator of the ENSO model and by employing the method of homotopic mapping, the approximate solution of equations for the corresponding ENSO model is studied. It is proved from the results that homotopic method can be used for analysing the sea surface temperature anomaly in the equatorial Pacific of the sea-air oscillator for the ENSO model.

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.

Asymptotic solving method for sea-air coupled oscillator ENSO model

The ENSO is an interannual phenomenon involved in the tropical Pacific ocean-atmosphere interaction. In this article, we create an asymptotic solving method for the nonlinear system of the ENSO model. The asymptotic solution is obtained. And then we can furnish weather forecasts theoretically and other behaviors and rules for the atmosphere- ocean oscillator of the ENSO.

The periodic solution to a delayed sea-air oscillator coupling model for the El Nifio-Southern Oscillation （ENSO）

A time-delay sea-air oscillator coupling model is studied. Using Mawhin＇s continuation theorem, the result on the existence of periodic solutions for the sea-air oscillator model is obtained.

DISTRIBUTION AND SEA-AIR FLUX OF BIOGENIC CLIMATIC GAS-DIMETHYLSULPHIDE(DMS)IN JIAOZHOU BAY

Concentrations of dimethylsulfide (DMS) and dimethylsulfoniopropionate (DMSP)were measured from Nov. 1995 to Sep. 1997 in seawater of Jiaozhou Bay, other ecological factors such aswater temperature, salinity, Chl a and zooplankton were also investigated. Distribution of DMS showedremarkable variation spatially and temporally, ranging from 0 .3 to 52. 3 nmol/L. Seasonally, DMS con-centration was high in late spring and low in autumn and winter. There was similar but weaker seasonalvariation of DMSP. Generaly, DMS concentraion inside the bay was higher than that outside the bay;

DISSOLVED OXYGEN DISTRIBUTION AND O_2 FLUXES ACROSS THE SEA-AIR INTERFACE IN EAST CHINA SEA WATERS

This study on the sectional and vertical distribution of dissolved oxygen （DO） and the O2 fluxes acrossthe sea-air interface in East China Sea （ESC） waters shows that the waters were in steady state and thatthe difference of DO was great in upper and bottom waters in Apr. 1994; but that seawater mixingwas strong and the difference of DO was small in upper and bettom waters in Oct. 1994. The above con-dusions were specially obvious in continental shelf waters under 100m. The DO maximum in subsurfacelayer waters appeared only at several stations and in general the DO in the waters decreased with depth.The horizontal distributions of O2 fluxes across the sea-air interface appeared in stripes in Leg 9404 whenmost regions covend were supersaturated with O2. seawater to air flux wn large, and that on section No.1was 1.594 L/m2·d. The horizontal distribution of O2 fluxes across the sea-air interface appeared lumpy inLeg 9410, when most regions covered were unsaturated with O2. O2 was dissolved from

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.

Environment drives the co-occurrence of bacteria and microeukaryotes in a typical subtropical bay

The co-occurrence of bacteria and microeukaryote species is a ubiquitous ecological phenomenon,but there is limited cross-domain research in aquatic environments.We conducted a network statistical analysis and visualization of microbial cross-domain co-occurrence patterns based on DNA sampling of a typical subtropical bay during four seasons,using high-throughput sequencing of both 18S rRNA and 16S rRNA genes.First,we found obvious relationships between network stability and network complexity indices.For example,increased cooperation and modularity were found to weaken the stability of cross-domain networks.Secondly,we found that bacterial operational taxonomic units(OTUs)were the most important contributors to network complexity and stability as they occupied more nodes,constituted more keystone OTUs,built more connections,more importantly,ignoring bacteria led to greater variation in network robustness.Gammaproteobacteria,Alphaproteobacteria,Bacteroidetes,and Actinobacteria were the most ecologically important groups.Finally,we found that the environmental drivers most associated with cross-domain networks varied across seasons(in detail,the network in January was primarily constrained by temperature and salinity,the network in April was primarily constrained by depth and temperature,the network in July was mainly affected by depth,temperature,and salinity,depth was the most important factor affecting the network in October)and that environmental influence was stronger on bacteria than on microeukaryotes.

Coupled Mode of Net Heat Flux and Wind Explain the Formation of the Indian Ocean Dipole

Net heat flux,sea surface temperature(SST),and sea surface wind in the northern Indian Ocean were investigated using the TropFlux,ERA5,and J-OFURO3 datasets and correlation analysis,power spectrum analysis,and singular value decomposition(SVD)methods,respectively.The relationships between net heat flux,SST,and sea surface winds were determined.The coupled modes of net heat flux and wind have been found to explain the SST variations in the Indian Ocean basin and the generation mechanism of the Indian Ocean Dipole(IOD).The correlation between net heat flux and wind is strongly negative.The SST lags the net heat flux and wind by approximately one month and has strong positive and negative correlations,respectively.The correlation between net heat flux and wind in the northern Indian Ocean is not only seasonal but also regionally dependent on seasonal variations.Using the SVD method,the important role of net heat flux in local sea-air interactions is discussed and identified as the initial perturbation that triggers the SST anomalies in the Southeast Indian Ocean,and the areas with key sea-air interactions and the generation mechanisms of the local sea-air interactions that form the IOD are determined.

Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation

Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.

A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

Ceramic tiles are one of the most indispensable materials for interior decoration.The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures.In this paper,we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network(GAN).The generated tile images can be tailored to meet the specific needs of the user for the tile textures.The proposed method consists of four steps.Firstly,a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on GAN.Secondly,for each ceramic tile image in the dataset,the corresponding sketch image is generated and then the mapping relationship between the images is trained based on a sketch extraction network using ResNet Block and jump connection to improve the quality of the generated sketches.Thirdly,the sketch style is redefined according to the characteristics of the ceramic tile images and then double cross-domain adversarial loss functions are employed to guide the ceramic tile generation network for fitting in the direction of the sketch style and to improve the training speed.Finally,we apply hidden space perturbation and interpolation for further enriching the output textures style and satisfying the concept of“one style with multiple faces”.We conduct the training process of the proposed generation network on 2583 ceramic tile images dataset.To measure the generative diversity and quality,we use Frechet Inception Distance(FID)and Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)metrics.The experimental results prove that the proposed model greatly enhances the generation results of the ceramic tile images,with FID of 32.47 and BRISQUE of 28.44.