Intrusion feature selection using Modified Heuristic Greedy Algorithm of Itemset

This paper proposes the Modified Heuristic Greedy Algorithm of Itemset (MHGIS) as a feature selection method for Network Intrusion Data. The proposed method can be use as an alternative method to gain the proper attributes for the proposed domain data: Network Intrusion Data. MHGIS is modified from...

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Bibliographic Details
Published in2013 13th International Symposium on Communications and Information Technologies (ISCIT) pp. 627 - 632
Main Authors Onpans, Janya, Rasmequan, Suwanna, Jantarakongkul, Benchaporn, Chinnasarn, Krisana, Rodtook, Annupan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2013
Subjects
Accuracy
Feature extraction
Feature Selection
Greedy algorithms
Heuristc Greedy
Intrusion detection
Itemsets
Network Intrusion Detection
Pattern Recognition
Principal component analysis
Support vector machines
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DOI10.1109/ISCIT.2013.6645936

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Summary:This paper proposes the Modified Heuristic Greedy Algorithm of Itemset (MHGIS) as a feature selection method for Network Intrusion Data. The proposed method can be use as an alternative method to gain the proper attributes for the proposed domain data: Network Intrusion Data. MHGIS is modified from original Heuristic Greedy Algorithm of Itemset (HGIS) to increase efficiency for finding proper feature. In our work, we compare our result with the common method of feature selection is which the Chi-Square (Chi 2 ) feature selection. There are 4 main steps in our experiment: Firstly, we start with data pre-processing to discard unnecessary attributes. Secondly, MHGIS feature selection and Chi 2 feature selection have been employed on the pre-processed data, to reduce the number of attributes. Thirdly, we measure the recognition performance by using supervised learning algorithms which are C4.5, BPNN, RBF and SVM. Lastly, we evaluate the results received from MHGIS and Chi 2 . From the KDDCup99 dataset, we got 13,499 randomly sampling patterns with 34 data dimensions. With the use of MHGIS and Chi 2 algorithms, we obtain 14 and 26 features respectively. The result shows that, the classification accuracies measure by C4.5 over the MHGIS selection algorithm produces better accuracies as compare to the Chi 2 feature selection and HGIS feature selection over all types of classification methods.
DOI:10.1109/ISCIT.2013.6645936