The objective of this paper is to propose and develop a hybrid intrusion detection system to handle series and non-series data by applying the two different concepts that are named clustering and autocorrelation function in a single architecture. There is a need to propose and build a system that can handle both types of data whether it is series or non-series. Therefore, the authors used two concepts to generate a robust approach to craft a hybrid intrusion detection system. The authors utilize an unsupervised clustering approach that is used to categorize the data based on domain similarity to handle non-series data and another approach is based on autocorrelation function to handle series data. The approach is consumed in single architecture where it carries data as input from both host-based intrusion detection systems and network-based intrusion detection systems. The result shows that the hybrid intrusion detection system is categorizing data based on the optimal number of clusters obtained through the elbow method in clustering.
In this paper, we propose a new intelligent prediction system to predict more accurately the presence of heart diseases effectively from feature-selected medical dataset. For this purpose, a new weighted genetic algorithm is proposed for selecting very important features from the dataset for improving the prediction accuracy of the disease. In this proposed intelligent prediction system, the data are preprocessed using the new weighted genetic algorithm and the new weighted fuzzy C-means clustering algorithm is used for effective fragmentation. Finally, we have used the ID3 algorithm for classification which is useful for making effective decision.
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