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针对复杂多步攻击检测问题,研究面向云计算环境的攻击场景构建方法。首先,构建了动态概率攻击图模型,设计了概率攻击图更新算法,使之能够随着时空的推移而周期性更新,从而适应弹性、动态性的云计算环境。其次,设计了攻击意图推断算法和最大概率攻击路径推断算法,解决了误报、漏报导致的攻击场景错误、断裂等不确定性问题,保证了攻击场景的准确性。同时将攻击场景随动态概率攻击图动态演化,保证了攻击场景的完备性和新鲜性。实验结果表明,所提方法能够适应弹性、动态的云计算环境,还原出攻击者完整的攻击渗透过程,重构出高层次的攻击场景,为构建可监管可追责的云环境提供了一定的依据和参考。 相似文献
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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. 相似文献
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目前安全问题已经成为阻碍云计算推广和发展的巨大障碍,云计算环境特有的数据和服务外包、虚拟化、多租户和跨域共享等特点使得其面临的安全威胁相比传统IT环境更复杂多样,对安全审计技术也提出了更高的要求。首先分析了云计算环境下安全审计面临的主要挑战,提出云环境下的安全审计参考框架,从用户维、业务维、数据维、设施维等4个维度上对云环境进行全方位的“体检”。然后针对不同维度,围绕日志审计、存储审计、配置审计3个方面的研究进行了评述,以期为我国未来云计算安全审计的发展研究提供有益的参考。 相似文献
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云计算环境下的攻击行为逐步表现出隐蔽性强、攻击路径复杂多步等特点,即一次完整的攻击需要通过执行多个不同的攻击步骤来实现最终目的。而现有的入侵检测系统往往不具有必要的关联能力,仅能检测单步攻击或攻击片段,难以发现和识别多步攻击模式,无法还原攻击者完整的攻击渗透过程。针对这一问题,提出了基于因果知识和时空关联的攻击场景重构技术。首先,利用贝叶斯网络对因果知识进行建模,从具有IP地址相关性的告警序列中发掘出具有因果关系的攻击模式,为后续关联分析提供模板依据。然后,借助因果知识网络,从因果、时间和空间多维度上对告警进行关联分析,以发现潜在的隐藏关系,重构出高层次的攻击场景,为构建可监管、可追责的云环境提供依据和参考。 相似文献
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针对现有无线射频识别(RFID)组证明协议易受到重放、跟踪等安全威胁及组证明效率较低的问题,提出了基于秘密共享密钥树的RFID组证明协议。协议设计了基于秘密共享方案的组证明密钥结构,将组密钥多次拆分生成密钥树增加了密钥结构的复杂度,加大了攻击者试图恢复组密钥的难度,提高了安全性;阅读器与每个标签只需一次交互便可以完成标签合法性认证及组证明信息的收集,提高了组证明效率。表明该协议较联合证明协议、基于椭圆曲线密码体制(ECC)的组证明协议、基于树的组证明协议等在安全和性能方面具有明显提高。 相似文献
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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。 相似文献
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