Abstract
Data mining is the taking out of concealed data from enormous databases (DBs); it is an effective innovation with unusual probable to enable associations to deliberate on the mainly imperative data in their data warehouses. IDS are the chief issue of the security which is helpful in everyday life to avoid the data from the attackers. Data mining includes numerous methods for the detection of intrusion which involves the detection of all harmful activities. In our proposed work, we initially apply KDD cup’99 dataset which is most broadly used method for detecting intrusion. DBSCAN is the most utilized method which is used to eliminate noise from the data. Then, we generate the most meaning inputs by analyzing and processing whole data which is done by the selection of feature method. K-means clustering performs grouping of data which is followed by SMO classifier. So we proposed a hybrid structure which improves the taken as a whole accuracy. MATLAB and WEKA tools are used to execute the whole process.
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Saxena, A., Saxena, K., Goyal, J. (2019). Hybrid Technique Based on DBSCAN for Selection of Improved Features for Intrusion Detection System. In: Rathore, V., Worring, M., Mishra, D., Joshi, A., Maheshwari, S. (eds) Emerging Trends in Expert Applications and Security. Advances in Intelligent Systems and Computing, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-2285-3_43
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DOI: https://doi.org/10.1007/978-981-13-2285-3_43
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