Increasing Feature Selection Accuracy through Recursive Method in Intrusion Detection System

  • Andreas Jonathan Silaban Telkom University
  • Satria Mandala Telkom University
  • Erwid Mustofa Jadied Telkom University
Abstract views: 463 , PDF downloads: 202


Artificial intelligence semi supervised-based network intrusion detection system detects and identifies various types of attacks on network data using several steps, such as: data preprocessing, feature extraction, and classification. In this detection, the feature extraction is used for identifying features of attacks from the data; meanwhile the classification is applied for determining the type of attacks. Increasing the network data directly causes slow response time and low accuracy of the IDS. This research studies the implementation of wrapped-based and several classification algorithms to shorten the time of detection and increase accuracy. The wrapper is expected to select the best features of attacks in order to shorten the detection time while increasing the accuracy of detection. In line with this goal, this research also studies the effect of parameters used in the classification algorithms of the IDS. The experiment results show that wrapper is 81.275%. The result is higher than the method without wrapping which is 46.027%.


Download data is not yet available.


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.

How to Cite
Silaban, A. J., Mandala, S., & Jadied, E. M. (2019). Increasing Feature Selection Accuracy through Recursive Method in Intrusion Detection System. International Journal on Information and Communication Technology (IJoICT), 4(2), 43-50.
Security & Cryptography