Many diverse groups have studied the insider threat problem, including government organizations such as the Secret Service, federally-funded research organizations such as RAND and CERT, and university researchers. In addition, many industry participants are interested in the problem, such as those in the financial sector. However, despite this interest, no consistent definition of an insider has emerged.We argue that the lack of a consistent definition of an insider hinders research in the detection of threats from insiders. In particular, a definition of an insider is first required in order to ensure that the research is, in fact, detecting threats of the desired type. Further, through the development and use of a consistent definition of insiders, it is possible to then compare  different detection approaches to determine the best approach for detecting particular types of insiders.

This position paper argues the need to develop provenances, and provenance systems, in such a way that errors in the provenance (whether deliberate or not) can be detected and corrected. The requirement that a provenance have high assurance leads to some suggestions about the way a provenance should be constructed.

Healthcare technologies have tended to focus on electronic health records and devices (e.g., devices within the home for patients or handheld devices for nurses and physicians), and the interaction between the two. However, no one to date has investigated how social networking technologies might be used to provide an assistive environment for patients who participate in group therapy. In this paper we propose such an environment and go on to discuss the privacy requirements and security implications in developing an appropriate support mechanism.

From a cyber-security perspective, attribution is considered to be the ability to determine the originating location for an attack. However, should such an attribution system be developed and deployed, it would provide attribution for all traffic, not just attack traffic. This has several implications for both the senders and receivers of traffic, as well as the intervening organizations, Internet service providers and nation-states. In this paper we examine the requirements for an attribution system, identifying all of the actors, their potential interests, and the resulting policies they might therefore have. We provide a general framework that represents the attribution problem, and outline the technical and policy requirements for a solution. We discuss the inevitable policy conflicts due to the social, legal and cultural issues that would surround such a system.

The insider threat problem is increasing, both in terms of the number of incidents and their financial impact. To date, solutions have been developed to detect specific instances of insider attacks (e.g., fraud detection) and therefore use very limited information for input. In this paper we describe an architecture for an enterprise-level solution that incorporates data from multiple sources. The unique aspects of this solution include the prioritization of resources based on the business value of the protected assets, and the use of psychological indicators and language affectation analysis to predict insider attacks. The goal of this architecture is not to detect that insider abuse has occurred, but rather to determine how to prioritize monitoring activities, giving priority to scrutinizing those whose background includes access to key combinations of assets as well as those psychological/other factors that have in the past been associated with malicious insiders.

Much of the literature on insider threat assumes, explicitly or implicitly, a binary, perimeter-based notion of an insider. However, it is generally accepted that this notion is unrealistic. The Attribute-Based Group Access Control (ABGAC) framework is a generalization of Role-Based Access Control (RBAC) which allows us to define a non-binary notion of "insiderness". In this paper, we illustrate how to use ABGAC to perform insider threat analysis of high-risk resources with three case studies. This precise yet flexible identification of high-risk resources and associated insiders allows organizations to understand where to target efforts towards defending against the insider problem.

The insider threat has long been considered one of the most serious threats in computer security, and one of the most difficult to combat. But the problem has never been defined precisely, and that lack of precise definition inhibits solutions. This paper presents a precise definition of insider threat, and shows how the definition enables an analysis of the set of problems traditionally lumped into "the insider threat". It introduces a hierarchy of policy abstractions, and argues that the discrepancies between the different layers of abstraction expose the potential for insider threat. It also presents a methodology for analyzing the threat based upon our definitions. In the process, we introduce Attribute-Based Group Access Control, a generalization of the Role-Based Access Control model that allows any attributes to define a group. We apply this to the insider threat by defining groups based on access capabilities, and using that to identify users with a high level of threat with respect to high-risk resources.