2 research outputs found
Optimizing a Model-Agnostic Measure of Graph Counterdeceptiveness via Reattachment
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
Let be a finite solvable permutation group acting faithfully and
primitively on a finite set .
Let be the stabilizer of a point
The rank of is defined as the number of orbits of in ,
including the trivial orbit .
In this paper, we completely classify the cases where has rank 5 and 6,
continuing the previous works on classifying groups of rank 4 or lower