Network security risks grow with increase in the network size. In recent past, the attacks on computer networks have increased tremendously and require efficient network intrusion detection mechanisms. Data mining and machine-learning techniques have been used for network intrusion detection during the past few years and have gained much popularity. In this paper, we propose an intrusion detection mechanism based on binary particle swarm optimization (PSO) and random forests (RF) algorithms called PSO-RF and investigate the performance of various dimension reduction techniques along with a set of different classifiers including the proposed approach. Binary PSO is used to find more appropriate set of attributes for classifying network intrusions, and RF is used as a classifier. In the preprocessing step, we reduce the dimensions of the dataset by using different state-of-the-art dimension reduction techniques, and then this reduced dataset is presented to the proposed PSO-RF approach that further optimizes the dimensions of the data and finds an optimal set of features. PSO is an optimization method that has a strong global search capability and is used here for dimension optimization. We perform extensive experimentation to prove the worth of the proposed approach by using different performance metrics. The standard benchmark, that is, KDD99Cup dataset, is used that contains the information about various kinds of network intrusions. The experimental results indicate that the proposed approach performs better than the other approaches for the detection of all kinds of attacks present in the dataset.

The aim of this paper is to investigate modified f(R, ϕ) theory of gravity, where R and ϕ represent the Ricci scalar and scalar potential respectively. Specifically, we take the spherically symmetric spacetime to discuss the possible emergence of compact stars. We study the physical behavior of compact stars by considering 4U 1820-30, SAX J 1808-3658 and Her X1, which are three popular models of compact stars. The graphical analysis of energy density, radial pressure, tangential pressure, energy conditions as well as stability of compact stars has been shown. It is concluded that behavior of these three stars is usual for f(R, ϕ) gravity models with some specific choices of model parameters.

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This work aims to investigate the behaviour of compact stars in the background of f (R, T) theory of gravity. For current work, we consider the Krori-Barua metric potential i.e., 𝝂(r) = Br 2 + C and 𝝀(r) = Ar 2 , where, A, B and C are constants. We use matching conditions of spherically symmetric space-time with Schwarzschild solution as an exterior geometry and examine the physical behaviour of stellar structure by assuming the exponential type f (R, T) gravity model. In the present analysis, we discuss the graphical behaviour of energy density, radial pressure, tangential pressure, equation of state parameters, anisotropy and stability analysis respectively. Furthermore, an equilibrium condition can be visualized through the modified Tolman-Oppenheimer-Volkov equation. Some extra features of compact stars i.e. mass-radius function, compactness factor and surface redshift have also been investigated. Conclusively, all the results in current study validate the existence of compact stars under exponential f (R, T) gravity model.

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Abstract-A major characteristic of text document classification problem is extremely high dimensionality of text data. In this paper we present four new algorithms for feature/word selection for the purpose of text classification. We use sequential forward selection methods based on improved mutual information criterion functions. The performance of the proposed evaluation functions compared to the information gain which evaluate features individually is discussed. We present experimental results using naive Bayes classifier based on multinomial model, linear support vector machine and k-nearest neighbor classifiers on the Reuters data set. Finally, we analyze the experimental results from various perspectives, including precision, recall and F1-measure. Preliminary experimental results indicate the effectiveness of the proposed feature selection algorithms in a text classification.

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