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- W2488896824 abstract "With recent advances in network based technology and the increased dependency of our every day life on this technology, assuring reliable operation of network based systems is very important. During recent years, a number of attacks on networks have dramatically increased and consequently interest in network intrusion detection has increased among the researchers. During the past few years, different approaches for collecting a dataset of network features, each with its own assumptions, have been proposed to detect network intrusions. Recently, many research works have been focused on better understanding of the network feature space so that they can come up with a better detection method. The curse of dimensionality is still a big obstacle in front of the researchers in network intrusion detection. In this chapter, DARPA’99 dataset is used for the study. Features in that dataset are analyzed with respect to their information value. Using the information value of the features, the number of dimensions in the data is reduced. Later on, using several clustering algorithms, effects of the dimension reduction on the dataset are studied and the results are reported." @default.
- W2488896824 created "2016-08-23" @default.
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- W2488896824 date "2011-11-02" @default.
- W2488896824 modified "2023-09-26" @default.
- W2488896824 title "Dimension Reduction and its Effects on Clustering for Intrusion Detection" @default.
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- W2488896824 doi "https://doi.org/10.4018/978-1-60960-836-1.ch007" @default.
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