Abstract
An Intrusion Detection System (IDS) seeks to identify unauthorized access to computer systems’ resources and data. The spreading of a data set size, in number of records as well as of attributes, as trigger the development of a number of big data platforms as well as parallel data analysis algorithms. This paper proposed a state-of-the-art technique to reduce the number of input features in dataset by using the Sequential Forward Selection (SFS) with k-Fold Cross Validation Model. Before reaching the feature reduction stage, the pre-processing analysis for detecting unusual observations that do not seem to belong to the pattern of variability produced by the other observations. The pre-processing analysis consists of outlier’s detection and Transformation. Outliers are best detected visually whenever this is possible. This paper explains the steps for detecting outliers’ data and describes the transformation method that transforms them to normality. The transformation obtained by maximizing Lamda functions usually improves the approximation to normality.
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Acknowledgments
The authors would like to thank the Riau University Indonesia for supporting this work. The work of Tutut Herawan is supported by Excellent Research Grant Scheme no vote O7/UTY-R/SK/0/X/2013 from Universitas Teknologi Yogyakarta, Indonesia.
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Dahliyusmanto, Herawan, T., Yulina, S., Abdullah, A.H. (2017). A Feature Selection Algorithm for Anomaly Detection in Grid Environment Using k-fold Cross Validation Technique. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_62
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DOI: https://doi.org/10.1007/978-3-319-51281-5_62
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