Abstract—Network traffic classification is the foundation of many network research works. In recent years, the research on traffic classification and identification based on machine learning method is a new research direction. Support Vector Machine (SVM) is the one of the machine learning method which performs good accuracy and stability. However, the traditional classification performance of...

Recently, many attack detection methods adopts machine learning algorithm to improve attack detection accuracy and automatically react to the attacks. However, the previous mechanisms based on machine learning have some disadvantages such as high false positive rate and computing overhead. In this paper, we propose a new DDoS detection model based on multiple SVMs (Support Vector Machine) in or...

The computer vision has become an emerging domain for machine learning ways. In the last decade, face recognition are developed for the image area to achieve correct and speedy performance. Now the face recognition technology (FRT) is in much advanced stage because research in this area is conducting continuously. The main reason of popularity is that it is using in many fields like identity au...

Support vector machines (SVMs) and fuzzy rule systems are functionally equivalent under some conditions. Therefore, the learning algorithms developed in the field of support vector machines can be used to adapt the parameters of fuzzy systems. Extracting fuzzy models from support vector machines has the inherent advantage that the model does not need to determine the number of rules in advance....

Bots, which are new malignant programs are hard to detect by signature based pattern matching techniques. In this research, we focused on a unique function of the bots the remote control channel (C&C session). We clarified that the C&C session has unique characteristics that come from the behavior of bot programs. Accordingly, we propose an alternative technique to identify computers compromise...

in this work some quantitative structure activity relationship models were developed for prediction of three bioenvironmental parameters of 28 volatile organic compounds, which are used in assessing the behavior of pollutants in soil. these parameters are; half-life, non dimensional effective degradation rate constant and effective péclet number in two type of soil. the most effective descripto...

In binary classification problems, two classes of data seem to be different from each other. It is expected more complicated due the clusters in class also tend different. Traditional algorithms as Support Vector Machine (SVM) or Twin (TWSVM) cannot sufficiently exploit structural information with cluster granularity data, cause limitation on capability simulation trends. Structural (S-TWSVM) e...