A feature selection algorithm for anomaly detection in grid environment using k-fold cross validation technique
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 algorithm...
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| Format: | Conference or Workshop Item |
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2017
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| author | Dahliyusmanto, Dahliyusmanto Herawan, Tutut Syefrida Yulina, Syefrida Yulina Abdullah, Abdul Hanan |
| author_facet | Dahliyusmanto, Dahliyusmanto Herawan, Tutut Syefrida Yulina, Syefrida Yulina Abdullah, Abdul Hanan |
| author_sort | Dahliyusmanto, Dahliyusmanto |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T21:10:40Z |
| format | Conference or Workshop Item |
| id | utm.eprints-97019 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:10:40Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | utm.eprints-970192022-09-13T07:01:11Z http://eprints.utm.my/97019/ A feature selection algorithm for anomaly detection in grid environment using k-fold cross validation technique Dahliyusmanto, Dahliyusmanto Herawan, Tutut Syefrida Yulina, Syefrida Yulina Abdullah, Abdul Hanan QA75 Electronic computers. Computer science 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. 2017 Conference or Workshop Item PeerReviewed Dahliyusmanto, Dahliyusmanto and Herawan, Tutut and Syefrida Yulina, Syefrida Yulina and Abdullah, Abdul Hanan (2017) A feature selection algorithm for anomaly detection in grid environment using k-fold cross validation technique. In: The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016, 18 - 20 August 2016, Bandung, Indonesia. http://dx.doi.org/10.1007/978-3-319-51281-5_62 |
| spellingShingle | QA75 Electronic computers. Computer science Dahliyusmanto, Dahliyusmanto Herawan, Tutut Syefrida Yulina, Syefrida Yulina Abdullah, Abdul Hanan A feature selection algorithm for anomaly detection in grid environment using k-fold cross validation technique |
| title | A feature selection algorithm for anomaly detection in grid environment using k-fold cross validation technique |
| title_full | A feature selection algorithm for anomaly detection in grid environment using k-fold cross validation technique |
| title_fullStr | A feature selection algorithm for anomaly detection in grid environment using k-fold cross validation technique |
| title_full_unstemmed | A feature selection algorithm for anomaly detection in grid environment using k-fold cross validation technique |
| title_short | A feature selection algorithm for anomaly detection in grid environment using k-fold cross validation technique |
| title_sort | feature selection algorithm for anomaly detection in grid environment using k fold cross validation technique |
| topic | QA75 Electronic computers. Computer science |
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