Intrusion Detection System Using Ensemble of Rule Learners and First Search Algorithm as Feature Selectors

Full Text (PDF, 174KB), PP.26-34

Views: 0 Downloads: 0

Author(s)

D P Gaikwad 1,*

1. AISSMS College of Engineering, Pune, Maharashtra, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2021.04.03

Received: 21 Jan. 2021 / Revised: 2 Mar. 2021 / Accepted: 17 Mar. 2021 / Published: 8 Aug. 2021

Index Terms

Homogeneous Classifier, PART, Jrip, Ensemble, Rule Learner

Abstract

Recently, the use of Internet is increased for digital communication to share a lot of sensitive information between computers and mobile devices. For secure communication, data or information must be protected from adversaries. There are many methods of safeties like encryption, firewalls and access control. Intrusion detection system is mainly used to detect internal attacks in organization. Machine leaning techniques are mostly used to implement intrusion detection system. Ensemble method of machine learning gives high accuracy in which moderately accurate classifiers are combined. Ensemble classifier also provides less false positive rates.
In this paper, a novel ensemble classifier using rule combination method has proposed for intrusion detection system. Ensemble classifier is designed using three rule learners as base classifiers. The benefits and feasibility of the proposed ensemble classifier have demonstrated by means of KDD’98 datasets. The main novelty of the proposed approach is based on three rule learner combination using rule of combination method of ensemble and feature selector. These three base classifiers are separately trained and combined using average probabilities rule combination. Base classifier’s accuracies have compared with the proposed ensemble classifier. Best First search algorithm has used to select relevant features from training dataset. This algorithm also helped to reduce dimension of training and testing dataset which benefits in reduction of training time. Several comparative experiments are conducted for evaluating performances of classifiers in term of accuracy and false positive rates. Experimental results show that the proposed ensemble classifier provide significant improvement of accuracy compared to individual classifiers with less positive rates.

Cite This Paper

D P Gaikwad, "Intrusion Detection System Using Ensemble of Rule Learners and First Search Algorithm as Feature Selectors", International Journal of Computer Network and Information Security(IJCNIS), Vol.13, No.4, pp.26-34, 2021. DOI: 10.5815/ijcnis.2021.04.03

Reference

[1] Reda M. et.al, “A Hybrid Network Intrusion Detection Framework Based on Random Forests and Weighted K-means”, Ain Shams Engineering Journal (2013) 4, 753–762, 2013.

[2] Barbara D at.el., “DAM: detecting intrusions by data mining”, In: Proc 2nd annu IEEE workshop in assure secure, New York; 2001. pp. 11–6, 2001.

[3] Zhang J et.al. “Random forest-based network intrusion detection systems”, IEEE Transactions on Systems, Man, and Cybernetics – Part C. Applications and Reviews 2008; 38(5):648–58, 2008.

[4] Snehlata S., Kapil Wankhade and Dongre,” Intrusion Detection System Using New Ensemble Boosting Approach”, International Journal of Modeling and Optimization, Vol. 2, No. 4, August 2012.

[5] Gaikwad D. P. and R. C. Thool, “Intrusion detection system using bagging ensemble method of machine learning”, In International Conference on Computing Communication Control and Automation, IEEE. pp. 291–295. doi:10.1109/ICCUBEA.2015.

[6] Yuyang Zhou, “Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier” , arXiv:1904.01352v4 [cs.CR] 2 Apr 2020.

[7] Breiman, L., “Bagging predictors. Machine learning”, 24, 123–140. Doi: 10.1007/BF00058655.

[8] Jasmeen K. Chahal, Amanjot Kaur,"A Hybrid Approach based on Classification and Clustering for Intrusion Detection System", International Journal of Mathematical Sciences and Computing, Vol.2, No.4, pp.34-40, 2016.

[9] Ian H. Witten, Eibe Frank and Mark A. Hall, “Data Mining Practical Machine Learning Tools and Techniques”, Third Edition, Morgan Kaufmann Publishers, 2011.

[10] Xiaofeng Zhao,Hua Jiang,LiYan Jiao,"A Data-Fusion-Based Method for Intrusion Detection System in Networks", International Journal of Information Engineering and Electronic Business, vol.1, no.1, pp.32-40, 2009.

[11] Saeed Khazaee, Karim Faez,"A Novel Classification Method Using Hybridization of Fuzzy Clustering and Neural Networks for Intrusion Detection", International Journal of Modern Education and Computer Science, vol.6, no.11, pp.11-24, 2014.

[12] Reena Sharma, Gurjot Kaur,"E-Mail Spam Detection Using SVM and RBF", International Journal of Modern Education and Computer Science, Vol.8, No.4, pp.57-63, 2016.

[13] Nabil, Ghizlane and Said El Hajji, “Neural Network-Based Voting System with High Capacity and Low Computation for Intrusion Detection in SIEM/IDS Systems”, In Security and Communication Networks Volume 2020, Article ID 3512737, pages-15,2020.

[14] Smitha Rajagopal et. al., “A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets”, Hindawi, Security and Communication Networks, 2020, Article ID 4586875, pages-9, https://doi.org/10.1155/2020/4586875. 2020.

[15] M. Govindarajan, “Evaluation of Ensemble Classifiers for Intrusion Detection,” World Academy of Science, International Journal of Computer and Information Engineering, Vol: 10, Issue No: 6, 2016.

[16] Habil Damania et.al. “MAIDEn: A Machine Learning Approach for Intrusion Detection using Ensemble Technique”, International Journal of Computer Applications, Volume 179 – No.13.2018.

[17] Ansam Khraisat et.al., “Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machin”, MDPI, Electronics 2020, Vol. 9, No. 173, 2020.

[18] Mrutyunjaya Panda and Manas Ranjan Patra, “Ensemble Voting System for Anomaly Based Network Intrusion Detection”, International Journal of Recent Trends in Engineering, Vol 2, No. 5, November 2016.

[19] Waweru Mwangi and Dr. Otieno Calvin, “Ensemble Network Intrusion Detection Model Based on Classification and Clustering for Dynamic Environment”, International Journal of Engineering Research and Technology, ISSN: 2278-0181, Vol. 7 Issue 02, February-2018.

[20] Ngoc Tu, Ernest Foo and Suriadi, “Improving Performance of Intrusion Detection System Using Ensemble Methods and Feature Selection”, Australasian Computer Science Week, ACSW, Brisbane, QLD, Australia. ACM, New York, NY, USA, 2018.

[21] Hariharan Rajadurai and Usha Devi Gandhi, “A stacked Ensemble Learning Model for Intrusion Detection in Wireless Network,” in Neural Computing and Applications, Springer-Verlag London Ltd., 2020.

[22] Mohammad Reza Parsaei, Samaneh Miri Rostami, Reza Javidan, “A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset”, International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 7, No. 6, 2016.

[23] V. Veeralakshmi, “Ripple down Rule learner (RIDOR) Classifier for IRIS Dataset,” In international Journal of Computer Science Engineering, ISSN: 2319-7323 Vol. 4 No.03, 2015.

[24] William Cohen, “Fast Effective Rule Induction Machine Learning,” in the Proceedings of the 12th International Conference, 2015.

[25] Helmut, Dieter and Michael, “Exploiting Partial Decision Trees for Feature Subset Selection in e-Mail Categorization”, in SAC’06, April 23-27, 2006, Dijon, France, 2006.

[26] Vaishali S. Parsania et.al., “Applying Naïve Bayes, BayesNet, PART, JRip and OneR Algorithmson Hypothyroid Database for Comparative Analysis”, in International Journal of Darshan institute of Engineering research and Emerging Technologies, Vol. 3, No. 1, 2014.