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Using HLA Object Models for the Analysis of Cross Domain Security Policies

机译:使用HLA对象模型进行跨域安全策略分析

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Across defence, training equipment, data and scenarios are likely to have different classification levels. Thus it is sometimes necessary for training to be carried out using a federation of participating systems running at different classification levels, but without compromising security rules. This is usually done using guards and filters to limit the data that may be released from the higher security domain to the lower security domain. In some cases, limiting the data may negatively impact the training and make it impossible to meet all the training goals. When following the process from design to security accreditation it is crucial to understand how to meet security requirements while also understanding the impact this will have on the training. This paper suggests an approach based on a description of the data exchange using the object models of the High Level Architecture. One type of object model is the Federation Object Model (FOM). It specifies the type and format of any data exchanged in the federation. This includes descriptions of objects (such as aircraft, soldiers and weapons) and interactions (such as orders, fire and detonation). Another type of object model is the Simulation Object Model (SOM). This is used to describe which objects and interactions are published (produced) and subscribed (consumed) by any one simulation system. The proposed method uses the SOMs to analyse the data flow within and between the different security domains. It allows the user to suggest different security policies. It then provides an automatic analysis that can be used to analyse the effect from both training and security perspective. This analysis can be performed for standard FOMs, like RPR FOM and NATO NETN FOM as well as extensions of these and project specific FOMs. The proposed method can be used as a basis for a dialog between accreditors and developers of training federations. This can help to avoid security issues, to understand the impact of training goals and also to detect any technical issues that may be introduced by the presence of a guard.
机译:横跨防御,培训设备,数据和情景可能具有不同的分类水平。因此,有时需要使用在不同分类级别运行的参与系统联合进行培训,但在不影响安全规则的情况下,可以进行培训。这通常是使用Guards和筛选器完成的,以限制可以从较高的安全域释放到较低安全域的数据。在某些情况下,限制数据可能会对培训产生负面影响,并使不可能满足所有培训目标。当从设计到安全认证的过程后,了解如何满足安全要求至关重要,同时也了解这将对培训有关。本文建议使用高级架构的对象模型的数据交换描述方法。一种类型的对象模型是联合对象模型(FOM)。它指定联合中交换的任何数据的类型和格式。这包括对象(例如飞机,士兵和武器)和互动的描述(例如订单,火灾和爆炸)。另一种类型的对象模型是模拟对象模型(SOM)。这用于描述任何一个仿真系统发布(产生)和订阅(消耗)的对象和交互。所提出的方法使用SOMS分析不同安全域内和之间的数据流。它允许用户建议不同的安全策略。然后,它提供了一种自动分析,可用于分析培训和安全视角的效果。可以对标准FOM进行此分析,如RPR FOM和NATO NetN FOM以及这些和项目特定FOM的扩展。该方法可以用作认证者和培训联合会开发人员之间对话的基础。这有助于避免安全问题,了解培训目标的影响,也可以检测可能通过保护的存在引入的任何技术问题。

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