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In this paper, we propose a hybrid method for intrusion detection which is based on k-means, naive-bayes and back propagation neural network (KBB). Initially we apply k-means which is partition-based, unsupervised cluster analysis method. In the form of clusters, we attain the gathered data which can be easily processed and learned by any machine learning algorithm. These outcomes are provided to...
In this paper, we propose a hybrid intrusion detection system that combines k-Means, and two classifiers: K-nearest neighbor and Naïve Bayes for anomaly detection. It consists of selecting features using an entropy based feature selection algorithm which selects the important attributes and removes the irredundant attributes. This algorithm operates on the KDD-99 Data set; this data set is used worldwide...
Traditional machine learning methods for intrusiondetection can only detect known attacks since these methodsclassify data based on what they have learned. New attacks areunknown and are difficult to detect because they have notlearned. In this paper, we present an improved k-meansclustering-based intrusion detection method, which trains onunlabeled data in order to detect new attacks. The result...
A fast clustering algorithm based on foregone samples for mixed data (FCABFS) in network anomaly detections technology is proposed in this paper. Original clustering center is exactly obtained by FCABFS through training foregone samples; Clustering center and non- similarity is calculated by separating objects. This algorithm solved problem of the higher false positive rate and the lower detection...
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