K-Means Clustering Approach to Analyze NSL-KDD Intrusion Detection Dataset

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Provided by: International Journal of Soft Computing and Engineering (IJSCE)
Topic: Security
Format: PDF
Clustering is the most acceptable technique to analyze the raw data. Clustering can help detect intrusions when the authors' training data is unlabeled, as well as for detecting new and unknown types of intrusions. In this paper, they are trying to analyze the NSL-KDD dataset using Simple K-Means clustering algorithm. They tried to cluster the dataset into normal and four of the major attack categories i.e. DoS, Probe, R2L, U2R. Experiments are performed in WEKA environment. Results are verified and validated using test dataset. Their main objective is to provide the complete analysis of NSL-KDD intrusion detection dataset.
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