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Design and Implementation of Secure Subnet Inside of Data Sensitive Network

机译:数据敏感网络内部安全子网的设计与实现

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Sensitive data leak can cause significant loss for some organizations, especially for technology intensive companies and country security departments. Traditional mandatory access control (MAC) can only control whether the user can access the sensitive data or not, and cannot prevent the user to leak or spread the data. So even designed impeccable access control policies, we still cannot prevent inside leak. A nature solution is using physical isolation to prevent sensitive data from being leaked outside network; however inside the physical isolated network, data still can be spread from one subnet to another. We present Secure Subnet System, a BLP model base security system that can provide more strong access control, which is called mandatory action control. In our system after a user read sensitive data, system will dynamically change security policies to prevent the user to leak these data or spread the data outside to another subnet. We use a state machine model to describe our system, and use secure transfer equations to dynamically calculate the system policies for each new state. Our model can be proved to be secure by formal methods. We implemented a demon of our system. In this paper we also show the design details of the demon and evaluate the demon both from security and performance. The evaluation results show that the output of the security tests case are under expected; and the performance test case show that, for the 64KB IO chunk size, IO read loss can be improved to 6.6%, IO write loss can be improved to 1.2% after optimization.
机译:敏感的数据泄漏可能会对某些组织造成重大损失,尤其是对于技术密集型公司和国家安全部门。传统的强制访问控制(MAC)仅可以控制用户是否可以访问敏感数据,而不能防止用户泄漏或传播数据。因此,即使设计了无可挑剔的访问控制策略,我们仍然无法防止内部泄漏。一个自然的解决方案是使用物理隔离来防止敏感数据泄漏到网络外部。但是,在物理隔离的网络内部,数据仍然可以从一个子网传播到另一个子网。我们提出了安全子网系统,它是一种BLP模型基础的安全系统,可以提供更强大的访问控制,这称为强制动作控制。在我们的系统中,用户读取敏感数据后,系统将动态更改安全策略,以防止用户泄漏这些数据或将数据散布到另一个子网之外。我们使用状态机模型来描述我们的系统,并使用安全传输方程式为每个新状态动态计算系统策略。我们的模型可以通过形式方法证明是安全的。我们实现了系统的恶魔。在本文中,我们还将展示恶魔的设计细节,并从安全性和性能两方面评估恶魔。评估结果表明,安全测试用例的输出低于预期。性能测试案例表明,对于64KB的IO块大小,优化后IO的读取损失可以降低到6.6%,IO的写入损失可以降低到1.2%。

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