Wrapper-Based Feature Selection Analysis For Semi-Supervised Anomaly Based Intrusion Detection System
Intrusion Detection System (IDS) plays as a role in detecting various types of attacks on computer networks. IDS identifies attacks based on a classification data network. The result of accuracy was weak in past research. To solve this problem, this research proposes using a wrapper feature selection method to improve accuracy detection. Wrapper-Feature selection works in the preprocessing stage to eliminate features. Then it will be clustering using a semi-supervised method. The semi-supervised method divided into two steps. There are supervised random forest and unsupervised using Kmeans. The results of each supervised and unsupervised will be ensembling using linear and logistic regression. The combination of wrapper and semi-supervised will get the maximum result.
Sharma, T., and Sinha, K. Intrusion detection system Technology, 2011.
T. M. Phuong, Z. Lin et R. B. Altman. Choosing SNPs using feature selection. Proceedings / IEEE Computational Systems Bioinformatics Conference, CSB. IEEE Computational Systems Bioinformatics Conference, pages 301-309, 2005. PMID 16447987
S. Revathi, D. A. M. A detailed analysis on nsl-kdd dataset using various machine learning techniques for intrusion detection. International Journal of Engineering Research Technology (IJERT) 2 (2013), 1-3.
B. Senthilnayaki, Dr.K.Venkatalakshmi, Dr.A.Kannan, Intrusion Detection Using Optimal Genetic Feature Selection and SVM based Classifier, 2015.
Alexander Hofinann." Feature Selection for Intrusion Detection: An Evolutionary Wrapper Approach". IEEE transactions on systems applications, 2004.
Mrutyunjaya Panda, M. R. P. Network intrusion detection using naIve bayes. IJCSNS International Journal of Computer Science and Network Security, 1-6.
Shah, S. C.; Kusiak, A. (2004). "Data mining and genetic algorithm based gene/SNP selection". Artificial intelligence in medicine. 31 (3): 183–196
Chuang, L.-Y.; Yang, C.-H "Tabu search and binary particle swarm optimization for feature selection using microarray data". Journal of computational biology. 16(12): 1689–1703, 2009
Uguz, H. A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowl.-Based Syst 24 (2011), 1024-1032
L.Dhanabal, D. S. S. A study on nsl-kdd dataset for Intrusion detection system based on classification algorithms. 1-3.
Copyright (c) 2020 Andreas Jonathan Silaban, Satria Mandala, Erwid Jadied Mustofa
This work is licensed under a Creative Commons Attribution 4.0 International License.Manuscript submitted to IJoICT has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals. Author(s) shall agree to assign all copyright of published article to IJoICT. Requests related to future re-use and re-publication of major or substantial parts of the article must be consulted with the editors of IJoICT.