一种新的云计算用户行为信任评估方法

Seyedeh Bahare Hosseini, Aida Shojaee, N. Agheli
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引用次数: 16

摘要

云计算是一种网络模型,用于对一组计算资源进行通用、简单和随需应变的访问,这些资源可以以最少的人工快速干扰,或者需要提供服务提供商。云计算中讨论的问题之一是用户对云服务的行为信任。对于云计算行为背景下的可信服务和用户满意度,需要评估每个用户的信任程度。云计算用户(包括成员、本地组织和分布式资源)应在相互关联的实体之间建立信任关系。人际关系建立在信任的基础上。本文提出了一种考虑用户识别码和mac地址的用户行为信任评估新方法,以防止注册多个用户名的成员,并使其他云计算环境中的用户行为信任信息可用。同样,在计算用户行为信任的数量时,对重复的破坏性行为给予负得分。该方法以6步测量中服务提供商角度的用户行为为依据,综合考虑了用户的新评分、过去的行为评分、其他实体的评分以及其他云计算提供商对用户的评分。提出了计算分数的原则,并将最终用户的总置信度作为分数获得。该方法有助于识别恶意用户和不良行为。最后,我们将该模型与其他使用原理的用户行为评估模型进行了比较,证明了该模型在检测恶意行为和用户方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new method for evaluating cloud computing user behavior trust
Cloud computing is a model for networking universal, easy and on demand access to a set of computing resources that can be quickly interfering with minimum labor, or require the service provider to be provisioned. One of the issues discussed in cloud computing, is user behavior trust in cloud services. For trusted services and users' satisfaction in the context of cloud computing behavior trust of each user should be assessed. Cloud computing subscribers, including members, local organizations and distributed resources should built trustworthy relationships among entities which are linked together. Relationships are based on trust. In this paper we proposed a new method for user behavior trust evaluation that considers user's identification code and mac address to prevent the registration of members with multiple user names and makes available the information of user behavior trust in other cloud computing environments. Also, in calculating the amount of user behavior trust assigns a negative score for repeated disruptive behaviors. This method rely on the user behavior from the perspective of service providers in the 6-step measures and considers the new scores of user, behavior scores rated in the past, the scores of the other entities and scores of other cloud computing provider's to the user. Principles are presented for calculating scores and total confidence to the end user is obtained as a score. The method helps identify malicious users and negative behaviors. Finally we compare proposed model with other models of user behavior evaluation that use principles and show the effectiveness of model in detecting malicious behavior and users.
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