Breaking Knapsack Cipher Using Population Based Incremental Learning - Kadhim, Nasreen J (Al-Nahrain Journal of Science, 2011)
The demand for effective internet security is increasing exponentially day by day. Businesses have an obligation to protect sensitive data from loss or theft. Such sensitive data can be potentially damaging if it is altered, destroyed or if it falls into the wrong hands. So they need to develop a scheme that guarantees to protect the information from attacker. Cryptology which is the science and study of systems for secret communication is at the heart of providing such guarantee. Breaking knapsack cipher using Population Based Incremental Learning (PBIL) is suggested in this paper. Results of this implementation are compared with results of Poonam Grag, Aditya Shastri, and D.C. Agarwal (2006) which had made an enhancement on the efficiency of genetic algorithm attack on knapsack cipher suggested by Spillman in 1993. The result of comparison shows that Population Based Incremental Learning has less complexity and enjoys high speed (i.e. correct results will be generated with less number of generation compared to that generated using genetic algorithm) also the overhead for genetic algorithm operations is significantly higher than for Population Based Incremental Learning.