DDoS (Distributed Denial-of-Service) attacks are among the cyberattacks that are increasing day by day and have caused problems for computer network servers. With the advent of SDN networks, they are not immune to these attacks, and due to the software-centric nature of these networks, this type of attack can be much more difficult for them, ignoring effective parameters such as port and Source IP in detecting attacks, providing costly solutions which are effective in increasing CPU load, and low accuracy in detecting attacks are of the problems of previously presented methods in detecting DDoS attacks. Given the importance of this issue, the purpose of this paper is to increase the accuracy of DDoS attack detection using the Second order correlation Coefficient technique based on ∅-entropy according to source IP and selection of optimal features. To select the best features, by examining the types of feature selection algorithms and search methods, the WrapperSubsetEval feature selection algorithm, the BestFirst search method, and the best effective features were selected. This study was performed on CTU-13 and ISOT datasets and the results were compared with other methods. The accuracy of the detection in this work indicates the high efficiency of the proposed approach compared to other similar methods.

Using the concepts of partial segregation theory, the effects of temperature and density on the Lennard-Jones parameters are evaluated and then a new relation is derived for the Second Virial Coefficient. The comparison made with the reported values for the Second Virial Coefficient substantiates the effectiveness of the proposed new relation based on aggregating fluid concepts.

In this paper, we present a detailed study of the following difference equation xn+1 = α, n 1 + xnxn−, 1, n ∈,N0, where the sequence (α, n)n≥, 0 is positive, real, periodic with period two and the initial values x−, 1, x0 are nonnegative real numbers. By this study, we determine global behavior of positive solutions of the above mentioned equation. We also give closed forms of its general solution.

Artificial neural networks are one of the new mathematical methods which have found many applications in chemical engineering in recent years. This method usually gives acceptable results when there is a lack of certain data.In this paper two different kinds of neural networks, multilayer perceptron (MLP) and radial basis function (RBF) are used to predict and estimate the Second virial Coefficient. Structural parameters and different methods for improving the training step and generality of these two neural networks have been investigated. Finally the behaviors of these two networks to give quick and accurate final results are compared with each other and the weak and strong points of each are discussed. In this paper we have used the experimental data of over 100 different hydrocarbons.

In this paper, some thermophysical properties of light and heavy water will be predicted and modeled using the virial equation of state (VEOS), and the effect of isotopic substitution will be studied. In this respect, by fitting the experimental and theoretical data of the Second virial Coefficient of light and heavy water in the temperature range of 300 to 1800 K, a new equation will be presented, using which the thermodynamic properties of the aforementioned fluids will be predicted and . The results are compared with the experimental data to evaluate the model and the new equation of the Second virial Coefficient. The results show that this approach has a very good ability to predict the thermophysical properties of light and heavy water. It will also demonstrate that deuterium isotopic substitution reduces the attractive interaction between molecules, especially above the critical temperature, and this causes the difference in the thermodynamic properties of the two fluids. The calculations performed in the above temperatures and pressure range using viral Coefficients to determine thermophysical properties of light and heavy water and also the investigation of the effect of isotopic substitution are novelties of this article.

An interaction potential at different orientation for the CH4 and CO2 complex was derived at the B3LYP level of theory and 6-31+G* basis sets. The potential energy surface was computed on some molecular geometries. The complete basis set limit of the interaction energies were fitted to well-known analytical functions. To determine the Second virial Coefficients B, U (r) is used to obtain the model’s parameters over the range of temperatures of interest.

This paper investigates the problem of whether all trajectories of the system x=h(y-F(x)) and y=-g(x) cross the vertical isocline, which is very important for the existence of periodic solutions and oscillation theory. Sufficient conditions are given for all trajectories to cross the vertical isocline.

In this work, a Genetic Algorithm boosted Least Square Support Vector Machine model by a set of linear equations instead of a quadratic program, which is improved version of Support Vector Machine model, was used for estimation of 98 pure compounds Second virial Coefficient. Compounds were classified to the different groups. Finest parameters were obtained by Genetic Algorithm method for training data. The accuracy of the Genetic Algorithm boosted Least Square Support Vector Machine was compared with four empirical equations that are well-known and are claimed can predict all compounds Second virial Coefficients (Pitzer, Tesonopolos, Gasanov RK and Long Meng). Results showed that in all classes of compounds, the Genetic Algorithm boosted Least Square Support Vector Machine method was more accurate than these empirical correlations. The Average Relative Deviation percentage of overall data set was 2. 53 for the Genetic Algorithm boosted Least Square Support Vector Machine model while the best Average Relative Deviation percentage for empirical models (Tesonopolos) was 15. 38. When the molecules become more complex, the difference in accuracy becomes sharper for empirical models where the proposed Genetic Algorithm boosted Least Square Support Vector Machine model have predicted good results for classes of compounds that empirical correlations usually fail to give good estimates.