Wrapper-Based Feature Selection Analysis For Semi-Supervised Anomaly Based Intrusion Detection System

  • Andreas Jonathan Silaban Telkom University
  • Satria Mandala Telkom University
  • Erwid Jadied Mustofa Telkom University
Abstract views: 341 , PDF downloads: 256

Abstract

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.

Downloads

Download data is not yet available.

References

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.

Published
2020-06-10
How to Cite
Silaban, A. J., Mandala, S., & Mustofa, E. J. (2020). Wrapper-Based Feature Selection Analysis For Semi-Supervised Anomaly Based Intrusion Detection System. International Journal on Information and Communication Technology (IJoICT), 5(2), 32-39. https://doi.org/10.21108/IJOICT.2019.52.209
Section
Security & Cryptography