In this paper an algorithm for speech encryption based on three dimension chaotic maps is proposed. The proposed algorithm consists of three main units: generation of keys, samples substitution and samples permutation process. In order to maximize the benefits of the substitution process, it is performed in two stages with cipher feedback, for the system. Moreover bit-level permutation for sample is introduced as substitution mechanism in the permutation stage. The Lorenz and Rossler chaotic system are employed as generation of keystream used for substitution and permutation process respectively. From the experimental results, it is concluded that the proposed algorithm has the advantages of very low residual intelligibility, key sensitivity and high quality recovered signal, and moreover the proposed algorithm can resist known-plaintext attacks and supports large key space make brute-force attacks infeasible.
In this paper a new speech encryption system is presented. It is based on permutation and substitution of speech samples using secret keys in time and transform domains. The system is with multilevel to increase the security and to present an encrypted signal with low residual intelligibility. The logistic map is employed in keys generation to generate permutation and mask keys to be used in the permutation and substitution process. In order to maximize the benefits of the permutation process for the system, Arnold cat map is applied to permute the samples in the last level of encryption system. Simulations results are presented in the paper indicate that the encryption system provides encryption speech signal of low residual intelligibility, key sensitivity and high quality recovered signal. Total key space for the proposed encryption system is (2^3 48 ), which is large enough to protect the encryption signal against brute-force attack.
In the field of data processing and analysis, the dataset may be a large set of features that restrict data usability and applicability, and thus the dimensions of data sets need to be reduced. Feature selection is the process of removing as much of the redundant and irrelevant features as possible from the original dataset to improve the mining process efficiency. This paper presented a study to evaluate and compare the effect of filter and wrapper methods as feature selection approaches in terms of classification accuracy and time complexity. The Naive Bayes Classifier and three classification datasets from the UCI repository are utilizing in the classification procedure. To investigate the effect of feature selection methods, they are applied to the different characteristics datasets to obtain the selected feature vectors which are then classified according to each dataset category. The datasets used in this paper are the Iris, Ionosphere, and Ovarian Cancer dataset. Experimental results indicate that the filter and wrapper methods provide approximately equal classification accuracy where the average accuracy value of the Ionosphere and Ovarian Cancer dataset is 0.78 and 0.91 for the same selected feature vectors respectively. For Iris dataset, the filter method outperforms the wrapper method by achieving the same accuracy value using only half number of selected features. The results also show that the filter method surpasses when considering the execution time.
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