Deterministic Learning Automata Solutions to the Equipartitioning Problem
IEEE Transactions on Computers
Learning automata: an introduction
Learning automata: an introduction
ACM Computing Surveys (CSUR)
Breaking substitution ciphers using a relaxation algorithm
Communications of the ACM
Cryptography and data security
Cryptography and data security
Learning Algorithms Theory and Applications
Learning Algorithms Theory and Applications
Graph Partitioning Using Learning Automata
IEEE Transactions on Computers
Efficient adaptive data compression using fano binary search trees
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
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Let Lambda be a finite plaintext alphabet and V be a cypher alphabet with the same cardinality as Lambda . In all one-to-one substitution cyphers, there exists the property that each element in V maps onto exactly one element in Lambda and vice versa. This mapping of V onto Lambda is represented by a function T*, which maps any v in V onto some lambda in Lambda (i.e., T*(v)= lambda ). The problem of learning the mapping of T* (or its inverse (T*)/sup -1/) by processing a sequence of cypher text is discussed. The fastest reported method to achieve this is a relaxation scheme that utilizes the statistical information contained in the unigrams and trigrams of the plaintext language. A new learning automaton solution to the problem called the cypher learning automaton (CLA) is given. The proposed scheme is fast, and the advantages of the scheme in terms of time and space requirements over the relaxation method have been listed. Simulation results comparing both cypher-breaking techniques are presented.