2 research outputs found

    Optimizing a Model-Agnostic Measure of Graph Counterdeceptiveness via Reattachment

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    Recognition of an adversary's objective is a core problem in physical security and cyber defense. Prior work on target recognition focuses on developing optimal inference strategies given the adversary's operating environment. However, the success of such strategies significantly depends on features of the environment. We consider the problem of optimal counterdeceptive environment design: construction of an environment which promotes early recognition of an adversary's objective, given operational constraints. Interpreting counterdeception as a question of graph design with a bound on total edge length, we propose a measure of graph counterdeceptiveness and a novel heuristic algorithm for maximizing counterdeceptiveness based on iterative reattachment of trees. We benchmark the performance of this algorithm on synthetic networks as well as a graph inspired by a real-world high-security environment, verifying that the proposed algorithm is computationally feasible and yields meaningful network designs.Comment: 15 pages, 11 figure

    Classifying Primitive Solvable Permutation Groups of Rank 5 and 6

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    Let GG be a finite solvable permutation group acting faithfully and primitively on a finite set Ω\Omega. Let G0G_0 be the stabilizer of a point α∈Ω\alpha \in \Omega The rank of GG is defined as the number of orbits of G0G_0 in Ω\Omega, including the trivial orbit {α}\{\alpha\}. In this paper, we completely classify the cases where GG has rank 5 and 6, continuing the previous works on classifying groups of rank 4 or lower
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