Intrusion detection system using an ant colony gene selection method based on information gain ratio using fuzzy rough sets

Document Type : Research Article

Authors

1 Department of Computer Science ,Shahid Bahonar University,Kerman,Iran

2 Computer Science Department, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract

With the development of network-based technologies, intrusion detection plays an important role in modern computer systems. Intrusion Detection System (IDS) is used to achieve higher security, and detect abnormal activities in computers or networks. The efficiency of intrusion detection systems mainly depends on the dimensions of data features. So, in the implementation of the IDS, by applying the feature selection phase irrelevant and redundant features are eliminated, and as a result, the speed and accuracy of the intrusion detection system increases. Applying appropriate search strategy and evaluation measure are significantly effective to feature selection. In this paper, we propose a feature selection method which uses a combination of filter and wrapper feature selection method. This method applies a modified ant colony algorithm as a search strategy on filter phase and fuzzy rough sets to calculate the information gain ratio and acquire the evaluation measure in the ant colony algorithm. Then, on the wrapper phase the minimal subsets of features with first order and second order accuracies are selected. To confirm the efficiency of our proposed method, we compared this method with three other methods and with a method which is based on artificial neural networks. Finally, we compared the proposed method with an ant colony optimization based method. Considering the results, the proposed method, on average, has a higher accuracy than the other methods and also selects a subset of features which have a minimum length.

Keywords

Main Subjects

References

[1] U. Ravale, N. Marathe, P. Padiya, "Feature Selection Based Hybrid Anomaly Intrusion Detection System Using K Means and RBF Kernel Function. ", In proceedings of the International Conference on Advanced Computing Technologies and Applications (ICACTA), pp. 428-435, 2015.
[2] A. A. Aburomman, M. B. I. Reaz, "A Novel Weighted Support Vector Machines Multiclass Classifier Based on Differential Evolution for Intrusion Detection Systems", Information Sciences, vol. 414, pp. 225-246, 2017.
[3] A. Sharma, I. Manzoor, N. Kumar, "A Feature Reduced Intrusion Detection System Using ANN Classifier", Expert Systems with Applications, vol. 88, pp. 249-257, 2017.
[4] Y. Zhu, J. Liang, J. Chen, Z. Ming, "An improved NSGA-III algorithm for feature selection used in intrusion detection", Knowledge-Based Systems, vol. 116, pp. 74-85, 2017.
[5] S. N. Ghazavi, T. W. Liao, "Medical data mining by fuzzy modeling with selected features", Artificial Intelligence in Medicine, vol. 43, pp. 195-206, 2008.
[6] T.N. Lal, O. Chapelle, J. Weston, A. Elisseeff, Embedded methods, in: I. Guyon, S. Gunn, M. Nikravesh, L.A. Zadeh (Eds.), Feature Extraction: Foundations and Applications. Studies in Fuzziness and Soft Computing, vol. 207, Springer, Berlin, Heidelberg, pp. 137–165, 2006.
[7] Ch. Khammassi, S. Krichen. "A GA-LR Wrapper Approach for Feature Selection in Network Intrusion Detection.", Computers & Security, 2017.
[8] Y. Y. Chung, N. Wahid, "A hybrid network intrusion detection system using simplified swarm optimization (sso)", Applied Soft Computing, vol. 12, pp. 3014–3022, 2012.
[9] E. De la Hoz, A. Ortiz, J. Ortega, A. Martínez-Álvarez, "Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps.", Knowledge-Based Systems, vol. 71, pp. 322–338, 2014.
[10] S. H. Kang, K. J. Kim, "A feature selection approach to find optimal feature subsets for the network intrusion detection system", Cluster Computing, pp. 1–9, 2016.

P. Maji, P. Garai, “On fuzzy-rough attribute selection: Criteria of Max-Dependency, Max-Relevance, MinRedundancy, and Max-Significance.”, applied soft computing, vol. 13, pp. 3968-3980, 2013. [12] Z. Pawlak, A. Skowron, “Rudiments of rough sets”, Information sciences, vol.177, pp. 3-27, 2007.

[13] G.A. Montazer, S. ArabYarmohammadi, "Detection of phishing attacks in Iranian e-banking using a fuzzy–rough hybrid system", Applied Soft Computing, vol. 35, pp. 482–492, 2015.
[14] M. Podsiadło & H. Rybiński, "Rough sets in economy and finance", Transactions on Rough Sets XVII, pp. 109-173, 2014.
[15] C.H. Xie, Y.J. Liu, J.Y. Chang, "Medical image segmentation using rough set and local polynomial regression", Multimedia Tools and Applications, vol. 74, pp. 1885–1914, 2015.
[16] V. Prasad, T.S. Rao, M.S. Babu, "Thyroid disease diagnosis via hybrid architecture composing rough data sets theory and machine learning algorithms", Soft Computing, vol. 20, pp. 1179–1189, 2016.
[17] M.P. Francisco, J.V. Berna-Martinez, A.F. Oliva, M.A.A. Ortega, "Algorithm for the detection of outliers based on the theory of rough sets", Decision Support Systems, vol. 75, pp. 63–75, 2015.
[18] J. Dai, Q. Xu, "Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification", Applied Soft Computing, vol. 13, pp. 1184-1199, 2012.
[19] F. Amiri, M. Rezaei, C. Lucus. A. Shakeri, N. Yazdani, "Mutual information-based feature selection for intrusion detection systems", Network and computer applications, vol. 34, pp. 1184-
1199, 2011.
[20] İ. Özçelik, R. R. Brooks, "Deceiving entropy based DoS detection", computers & security, vol. 48, pp. 234-245, 2015.
[21] Sh. Aljawarneh, M. Aldwairi, M. B. Yassein, "Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model", Computational Science, 2017.
[22] M. Dorigo, L. M. Gambardella, "A cooperative learning approach to the traveling salesman problem", IEEE Transactions on Evolutionary Computation, vol. 1, pp. 53-66, 1997.
[23] A. Shenfield, D. Day, A. Ayesh, "Intelligent intrusion detection systems using artificial neural networks.", the Korean Institute of Communications and Information Sciences, vol. 4, pp. 95-99, 2018.
[24] M. Hosseinzadeh, P. Kabiri. "Feature selection for intrusion detection system using ant colony optimization", International Journal of Network Security, vol. 18, pp. 420-432, 2016.
AUT Journal of Modeling and Simulation
Volume 51, Issue 1
June 2019
Pages 33-44

Files

Share

How to cite

Statistics