Current trust models enable decision support at an implicit level by means of thresholds or constraint satisfiability. Decision support is mostly included only for a single binary action, and does not explicitly consider the purpose of a transaction. In this paper, we present a game theoretic model that is specifically tuned for decision support on a whole host of actions, based on specified thresholds of risk. As opposed to traditional representations on the real number line between 0 and +1, Trust in our model is represented as an index into a set of actions ordered according to the agent's preference. A base scenario of zero trust is defined by the equilibrium point of a game described in normal form with a certain payoff structure. We then present the blind trust model, where an entity attempts to initiate a trust relationship with another entity for a one-time transaction, without any prior knowledge or recommendations. We extend this to the incentive trust model where entities can offer incentives to be trusted in a multi-period transaction. For a specified risk threshold, both models are analyzed by using the base scenario of zero trust as a reference. Lastly, we present some issues involved in the translation of our models to practical scenarios, and suggest a rich set of extensions of the generalized game theoretic approach to model decision support for existing trust frameworks.
A Document Management System (DMS) is a repository of digital documents that provides functionality for check-in, check-out and shared editing. In a DMS, security mechanisms like encryption of documents and enforcement of policies are implemented to protect from information leakage. These security schemes, essentially applications of Digital Rights Management technologies, while effective against external attacks, are ineffective against insider attacks. The typical insider in a DMS already has access to documents and hence, his capabilities for information leakage are much higher. In this work, we address an important, yet unexplored problem of masquerading users in a DMS, a threat for which the DMS inherently has no protection. We approach the problem by monitoring the pattern and mannerism of user actions on documents and building a profile of each user using the resulting logs. In order to illustrate our ideas, we built user profiles of 41 users working on Microsoft Word and applied two algorithms, viz., IPAM and Naïve Bayes to distinguish between them. When supplied with appropriately interpreted command sequences of a DMS, IPAM was able to distinguish between users effectively, while Naïve Bayes failed to produce any meaningful results. We recorded an average detection rate of 58% with a false positive of 14%.
A document management system (DMS) provides for secure operations on a distributed repository of digital documents. This paper presents a two-phase approach to address the problem of locating the sources of information leaks in a DMS. The initial monitoring phase treats user interactions in a DMS as a series of transactions, each involving content manipulation by a user; in addition to standard audit logging, relevant contextual information and user-related metrics for transactions are recorded. In the detection phase, leaked information is correlated with the existing document repository and context information to identify the sources of leaks. The monitoring and detecting phases are incorporated in a forensic extension module (FEM) to a DMS to combat the insider threat.
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