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Abstract

Permission-based security models provide controlled access to various system resources. The expressiveness of the permission set plays an important role in providing the right level of granularity in access control. In this work, we present a methodology for the empirical analysis of permission-based security models which makes novel use of the Self-Organizing Map (SOM) algorithm of Kohonen (2001). While the proposed methodology may be applicable to a wide range of architectures, we analyze 1,100 Android applications as a case study. Our methodology is of independent interest for visualization of permissionbased systems beyond our present Android-specific empirical analysis. We offer some discussion identifying potential points of improvement for the Android permission model, attempting to increase expressiveness where needed without increasing the total number of permissions or overall complexity.

This review describes a body of work on computational immune systems that behave analogously to the natural immune system. These artificial immune systems (AIS) simulate the behavior of the natural immune system and in some cases have been used to solve practical engineering problems such as computer security. AIS have several strengths that can complement wet lab immunology. It is easier to conduct simulation experiments and to vary experimental conditions, for example, to rule out hypotheses; it is easier to isolate a single mechanism to test hypotheses about how it functions; agent-based models of the immune system can integrate data from several different experiments into a single in silico experimental system. Keywords: artificial immune system, agent-based models, in silico modeling, computational immunology