Statistical cryptanalysis of DFT-based speech scramblers

This paper presents an automated attack for cryptanalysis of DFT-based analog speech scramblers. In the proposed system, the discrete key is imbedded in a continuous keyspace and the key search is realized in the maximum likelihood estimation process. For greater efficiency, we also develop an iterative maximization algorithm which takes into account the doubly stochastic characteristics of the underlying keyspace. It is shown that the statistical cryptanalysis appears useful in breaking the DFT-based speech scramblers with fixed as well as varying permutation.