The discipline of enterprise architecture advocates the use of models to support decision-making on enterprise-wide information system issues. In order to provide such support, enterprise architecture models should be amenable to analyses of various properties, as e.g. the level of enterprise information security. This paper proposes the use of a formal language to support such analysis. Such a language needs to be able to represent causal relations between, and definitions of, various concepts as well as uncertainty with respect to both concepts and relations. To support decision making properly, the language must also allow the representation of goals and decision alternatives. This paper evaluates a number of languages with respect to these requirements, and selects influence diagrams for further consideration. The influence diagrams are then extended to fully satisfy the requirements. The syntax and semantics of the extended influence diagrams are detailed in the paper, and their use is demonstrated in an example.
Many academic disciplines have general theories, which apply across the discipline and underlie much of its research. Examples include the Big Bang theory (cosmology), Maxwell's equations (electrodynamics), the theories of the cell and evolution (biology), the theory of supply and demand (economics), and the general theory of crime (criminology). Software engineering, in contrast, has no widely-accepted general theory. Consequently, the SEMAT Initiative organized a workshop to encourage development of general theory in software engineering. Workshop participants reached broad consensus that software engineering would benefit from better theoretical foundations, which require diverse theoretical approaches, consensus on a primary dependent variable and better instrumentation and descriptive research.
Information system security risk, defined as the product of the monetary losses associated with security incidents and the probability that they occur, is a suitable decision criterion when considering different information system architectures. This paper describes how probabilistic relational models can be used to specify architecture metamodels so that security risk can be inferred from metamodel-instantiations. A probabilistic relational model contains classes, attributes, and class-relationships. It can be used to specify architectural metamodels similar to class diagrams in the Unified Modeling Language. In addition, a probabilistic relational model makes it possible to associate a probabilistic dependency model to the attributes of classes in the architectural metamodel. This paper proposes a set of abstract classes that can be used to create probabilistic relational models so that they enable inference of security risk from instantiated architecture models. If an architecture metamodel is created by specializing the abstract classes proposed in this paper, the instantiations of the metamodel will generate a probabilistic dependency model that can be used to calculate the security risk associated with these instantiations. The abstract classes make it possible to derive the dependency model and calculate security risk from an instance model that only specifies assets and their relationships to each other. Hence, the person instantiating the architecture metamodel is not required to assess complex security attributes to quantify security risk using the instance model.
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