基于上下文感知的自适应访问控制模型

Context-aware adaptive access control model

面对日益复杂和动态的访问场景,传统访问控制显现出灵活性不足的局限性,一定程度上损害了资源的可用性。针对这一问题,提出一种基于上下文感知的自适应访问控制模型。通过提供额外的特殊授权机制,提升面对特殊请求的灵活性和时间效率,提高资源的可用性。使用基于模糊逻辑的上下文感知推理方法评估用户访问时的上下文情景,使系统能够据此作出特殊授权决策,实现了访问控制系统在动态环境下灵活、细粒度的授权能力。通过引入信任度机制限制用户的特殊授权,防止权限滥用。同时对用户在会话期间的活动进行跟踪与监测,以提供自适应访问控制能力。实验结果表明,该模型能够适应动态、复杂的访问控制场景,可以根据不同情境动态调整权限,提高了资源的可用性。

In the face of increasingly complex and dynamic access scenarios,traditional access control exhibits limitations in flexibility,to some extent impairing the availability of resources.To address this issue,this paper proposed a context-aware adaptive access control model(CABAC).This model enhances flexibility and temporal efficiency by incorporating additional special authorization mechanisms to handle specific requests,ultimately improving resource availability.Utilizing a context-aware reasoning approach grounded in fuzzy logic to evaluate the contextual situation during user access,the system could make special authorization decisions,thereby achieving a flexible and fine-grained authorization capability suitable for dynamic environments.Introducing a trust mechanism confined users’special authorizations,preventing misuse of privileges.Simultaneously,tracking and monitoring user activities during sessions were conducted to provide adaptive access control capability.Experimental results demonstrate that CABAC can adapt to dynamic and complex access control scenarios,dynamically adjusting permissions according to different contexts,thereby enhancing resource availability.