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As the new technologies are emerging, data is getting generated in larger volumes high dimensions. The high dimensionality of data may rise to great challenge while classification. The presence of redundant features and noisy data degrades the performance of the model. So, it is necessary to extract the relevant features from given data set. Feature extraction is an important step in many machine learning algorithms. Many researchers have been attempted to extract the features. Among these different feature extraction methods, mutual information is widely used feature selection method because of its good quality of quantifying dependency among the features in classification problems. To cope with this issue, in this paper we proposed simplified mutual information based feature selection with less computational overhead. The selected feature subset is experimented with multilayered perceptron on KDD CUP 99 data set with 2- class classification, 5-class classification and 4-class classification. The accuracy is of these models almost similar with less number of features.

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The secure transmission of any form of data over a communication medium is prime important across the globe or in research arena. Cryptography is a branch of cryptology and it provides security for data transmission between any communicating parties. The Hill cipher is one of the symmetric key substitution algorithms. Hill Cipher is vulnerable to known plaintext attack. This paper presents an enhancement to the Hill cipher by utilizing the circulant matrices. The proposed technique shares a prime circulant matrix as a secret key and we choose a non-singular matrix as a public key in such way that the determinant of the coefficient matrix is zero. Computational cost shows that the proposed technique is efficient and it thwarts all the security attacks.

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