Abstract
Cross-domain information exchange is an increasingly important capability for conducting efficient and secure operations, both within coalitions and within single nations. A data guard is a common cross-domain sharing solution that inspects the security labels of exported data objects and validates that they are such that they can be released according to policy. While we see that guard solutions can be implemented with high assurance, we find that obtaining an equivalent level of assurance in the correctness of the security labels easily becomes a hard problem in practical scenarios. Thus, a weakness of the guard-based solution is that there is often limited assurance in the correctness of the security labels. To mitigate this, guards make use of content checkers such as dirty word lists as a means of detecting mislabeled data. To improve the overall security of such cross-domain solutions, we investigate more advanced content checkers based on the use of machine learning. Instead of relying on manually specified dirty word lists, we can build data-driven methods that automatically infer the words associated with classified content. However, care must be taken when constructing and deploying these methods as naive implementations are vulnerable to manipulation attacks. In order to provide a better context for performing classification, we monitor the incoming information flow and use the audit trail to construct controlled environments. The usefulness of this deployment scheme is demonstrated using a real collection of classified and unclassified documents.
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