基于GNN双源学习的访问控制关系预测方法

With the rapid development and wide application of big data technology, users’ unauthorized access to resources becomes one of the main problems that restrict the secure sharing and controlled access to big data resources. The ReBAC (Relationship-Based Access Control) model uses the relationship between entities to formulate access control rules, which enhances the logical expression of policies and realizes dynamic access control. However, It still faces the problems of missing entity relationship data and complex relationship paths of rules. To overcome these problems, a link prediction model LPMDLG based on GNN dual-source learning was proposed to transform the big data entity-relationship prediction problem into a link prediction problem with directed multiple graphs. A topology learning method based on directed enclosing subgraphs was designed in this modeled. And a directed dual-radius node labeling algorithm was proposed to learn the topological structure features of nodes and subgraphs from entity relationship graphs through three segments, including directed enclosing subgraph extraction, subgraph node labeling calculation and topological structure feature learning. A node embedding feature learning method based on directed neighbor subgraph was proposed, which incorporated elements such as attention coefficients and relationship types, and learned its node embedding features through the sessions of directed neighbor subgraph extraction and node embedding feature learning. A two-source fusion scoring network was designed to jointly calculate the edge scores by topology and node embedding to obtain the link prediction results of entity-relationship graphs. The experiment results of link prediction show that the proposed model obtains better prediction results under the evaluation metrics of AUC-PR, MRR and Hits@N compared with the baseline models such as R-GCN, SEAL, GraIL and TACT. The ablation experiment results illustrate that the model’s dual-source learning scheme outperforms the link prediction effect of a single scheme. The rule matching experiment results verify that the model achieves automatic authorization of some entities and compression of the relational path of rules. The model effectively improves the effect of link prediction and it can meet the demand of big data access control relationship prediction. © 2022, Beijing Xintong Media Co., Ltd.. All rights reserved.     

一种面向云服务组合的策略冲突检测机制

针对云服务组合的策略冲突问题,研究了云服务中属性间关系及组件服务间组合关系的特点,提出了基本类型冲突、层次关系冲突、互斥关系冲突、组合关系约束冲突四种冲突类型;设计了能够直观表达策略中多种关系的策略生成图模型,该模型具有结构灵活、易于更新和动态扩展的优点;将冲突检测问题转化为图的连通性问题,提出了一种基于策略生成图模型的冲突检测机制,实现了对云环境下大规模策略集中冲突策略的高效检测。最后,开展仿真实验,验证、评估了检测机制的有效性和检测效率。     

复杂物联网联盟链系统通信机制研究EI北大核心CSCD

联盟链具有公有链固有的安全性,其许可准入机制允许对网络结点及规模进行控制,恰好迎合了物联网(Internet of things,IoT)向规模化、智能化发展的需要,成为物联网学术界研究的热点.然而,联盟链在一定程度上违背了区块链去中心化价值和信任体系,产生了多中心化的复杂区块链生态体系,为使物联网数字资产在不同联盟链间安全、自主、动态流转,迫切需要对涉及多个联盟链的复杂系统通信机制进行研究.基于存在多个特权子群的门限数字签名机制建立多联盟链链间合作共识,利用授权码构造身份证明,实现链间实体自主授权过程;构建跨联盟链交易原子提交协议,确保异步授权状态同步;提出多级混合可选信任—验证交易共识机制.实验表明,上述机制能够在优化系统性能的同时确保系统的安全性.     

区块链技术及其在信息安全领域的研究进展

区块链是一种源于数字加密货币比特币的分布式总账技术,其发展引起了产业界与学术界的广泛关注.区块链具有去中心化、去信任、匿名、数据不可篡改等优势,突破了传统基于中心式技术的局限,具有广阔的发展前景.介绍了区块链技术在信息安全领域的研究现状和进展.首先,从区块链的基础框架、关键技术、技术特点、应用模式、应用领域这5个方面介绍了区块链的基本理论与模型;然后,从区块链在当前信息安全领域研究现状的角度出发,综述了区块链应用于认证技术、访问控制技术、数据保护技术的研究进展,并对比了各类研究的特点;最后,分析了区块链技术的应用挑战,对区块链在信息安全领域的发展进行了总结与展望,希望对未来进一步的研究工作有一定的参考价值.     

基于区块链的大数据访问控制机制

针对大数据资源来源广泛、动态性强且呈现出分布式管理的特点,当前主流集中式访问控制机制存在权限管理效率低、灵活性不足、扩展性差等不足.基于此,以ABAC模型为基础,提出一种基于区块链的大数据访问控制机制：首先,对区块链技术的基本原理进行描述,并对基于属性的访问控制模型进行形式化的定义;然后提出基于区块链技术的大数据访问控制架构,并对访问控制的基本框架与流程进行了详细的阐述与分析;同时,对基于区块链事务的访问控制策略及实体属性信息管理方法进行了说明,以此保证访问控制信息的不可篡改性、可审计性和可验证性;随后,采用基于智能合约的访问控制方法实现对大数据资源由用户驱动、全程透明、动态、自动化的访问控制;最后,通过仿真实验验证了该机制的有效性,并对该研究内容进行总结与展望.     

基于访问控制日志的访问控制策略生成方法

为解决其他访问控制机制向基于属性的访问控制机制迁移过程中所面临的策略生成问题,该文提出一种基于访问控制日志的访问控制策略生成方法,利用基于机器学习分类器的递归属性消除法实现策略属性的选择,基于信息不纯度从日志记录中提炼出蕴含的属性-权限关系,结合实体属性选择的结果,构建策略结构树,实现基于属性的访问控制(ABAC)策略的生成,并设计了基于二分搜索的策略生成优化算法实现对最优策略生成结果的快速计算。实验结果表明,只需原始实体属性集中32.56%的属性信息即可实现对日志中95%的策略覆盖,并且能够将策略规模压缩为原有规模的33.33%,证实了该方案的有效性,能够为ABAC策略管理提供有力支撑。