ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Modified Bee Colony with Bacterial Foraging Optimization based Hybrid Feature Selection Technique for Intrusion Detection System Classifier Model


Affiliations
1 School of Computer Science and Engineering, Bharathidasan University, India
     

   Subscribe/Renew Journal


Feature selection (FS) plays an essential role in creating machine learning models. The unrelated characteristics of the data disturb the precision of the perfection and upsurges the training time required to build the model. FS is a significant process in creating the Intrusion Detection System (IDS). In this document, we propose a technique for selecting container functions for IDS. To develop the performance capacity of the modified Artificial Bee Colony (ABC) procedure, a hybrid method is presented in which the swarm behavior of the Bacterial Foraging Optimization (BFO) algorithm is entered into the Modified Bee Colony (MBC) procedure to perform a local search. The proposed Hybrid MBC-BFO algorithm is analyzed with three different classification techniques which are separately analyzed to verify the proposed performance. The classification techniques are Artificial Neural Networks (ANN), Recursive Neural Network (ReNN), and Recurrent Neural Network (RNNs). The proposed algorithm has passed several algorithms for selecting advanced functions in terms of detection accuracy, recall, precision, false positive rate, and F-score.

Keywords

Swarm Intelligence, Modified Bee Colony, Bacterial Foraging Optimization, Feature Selection, IDS, KDDCUP’99.
Subscription Login to verify subscription
User
Notifications
Font Size

  • H. Liu and H. Motoda, “Feature Selection for Knowledge Discovery and Data Mining”, Kluwer Academic Publishers, 2012.

  • X. Tang, Y. Dai and Y. Xiang, “Feature Selection based on Feature Interactions with Application to Text Categorization”, Expert Systems with Applications, Vol. 120, pp. 207-216, 2019.

  • K. Scarfone aand P. Mell, “Guide to Intrusion Detection and Prevention Systems (IDPS)”, Technical Report, National Institute of Standards and Technology, pp. 1-78, 2012.

  • S. Mohammadi, H. Mirvaziri, M. Ghazizadeh Ahsaee and H. Karimipour, “Cyber Intrusion Detection by Combined Feature Selection Algorithm”, Journal of Information Security and Applications, Vol. 44, No. 2, pp. 80-88, 2019.

  • S. Zaman and F. Karray, “Features Selection for Intrusion Detection Systems based on Support Vector Machines”, Proceedings of 6th IEEE International Conference on Consumer Communications and Networking, pp. 1-8, 2009.

  • S. Maza and M. Touahria, “Feature Selection Algorithms in Intrusion Detection System: A Survey”, KSII Transactions on Internet and Information Systems, Vol. 12, No. 10, pp. 1-14, 2018.

  • K. Chen, F.Y. Zhou and X.F. Yuan, “Hybrid Particle Swarm Optimization with Spiral-Shaped Mechanism for Feature Selection”, Expert Systems with Applications, Vol. 128, pp. 140-156, 2019.

  • M. Keshtgary and N. Rikhtegar, N., “Intrusion Detection Based on a Novel Hybrid Learning Approach”, Journal of AI and Data Mining, Vol. 6, No. 1, pp. 157-162, 2018.

  • N. Acharya and S. Singh, “An IWD-Based Feature Selection Method for Intrusion Detection System.”, Soft Computing, Vol. 22, No. 13, pp. 407-416, 2018.

  • A.S. Eesa, Z. Orman and A.M.A. Brifcani, “A New Feature Selection Model Based on ID3 and Bees Algorithm for Intrusion Detection System”, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 23, No. 2, pp. 615-622, 2015.

  • E. Zorarpaci and S.A. Ozel, “A Hybrid Approach of Differential Evolution and Artificial Bee Colony for Feature Selection”, Expert Systems with Applications, Vol. 62, pp. 91-103, 2016.

  • Barnali Sahu,Satchidananda Dehuri and Alok Jagadev, “A Study on the Relevance of Feature Selection Methods in Microarray Data”, The Open Bioinformatics Journal, Vol. 11, No. 2, pp. 117-139, 2018.

  • Swagatam Das, Arijit Biswas, Sambarta Dasgupta and Ajith Abraham, “Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications”, Foundations of Computational Intelligence, Vol. 3, pp. 23-55, 2009.

  • L.P. Dias and J.J.F. Cerqueira, “Using Artificial Neural Network in Intrusion Detection Systems to Computer Networks”, Proceedings of 9th IEEE International Conference on Computer Science and Electronic Engineering, pp. 1-8, 2017.

  • J. Martens and I. Sutskever, “Learning Recurrent Neural Networks with Hessian-Free Optimization”, Proceedings of 28th IEEE International Conference on Machine Learning, pp. 1-6, 2011.

  • Xuegong Zhang, Xin Lu and Qian Shi, “Recursive SVM Feature Selection and Sample Classification for Mass-Spectrometry and Microarray Data”, BMC Bioinformatics, Vol. 7, No. 19, pp. 1-18, 2006.

  • V.R. Shewale and H.D. Patil, “Performance Evaluation of Attack Detection Algorithms using Improved Hybrid IDS with Online Captured Data”, International Journal of Computer Applications, Vol. 146, No. 8, pp. 1-12, 2016.

  • S. Kalaivani and Gopinath Ganapath, “Bio-Inspired Modified Bees Colony Feature Selection based Intrusion Detection System for Cloud Computing Application”, International Journal of Advanced Science and Technology, Vol. 29, No. 3, pp. 1-12, 2020.

  • S. Kalaivani and Gopinath Ganapath, “Bacterial Foraging Optimization for Enhancing the Security in Intrusion Detection System”, International Journal of Scientific and Technology Research, Vol. 9, No. 2, pp. 1-8, 2020.


Abstract Views: 237

PDF Views: 0




  • Modified Bee Colony with Bacterial Foraging Optimization based Hybrid Feature Selection Technique for Intrusion Detection System Classifier Model

Abstract Views: 237  |  PDF Views: 0

Authors

S. Kalaivani
School of Computer Science and Engineering, Bharathidasan University, India
G. Gopinath
School of Computer Science and Engineering, Bharathidasan University, India

Abstract


Feature selection (FS) plays an essential role in creating machine learning models. The unrelated characteristics of the data disturb the precision of the perfection and upsurges the training time required to build the model. FS is a significant process in creating the Intrusion Detection System (IDS). In this document, we propose a technique for selecting container functions for IDS. To develop the performance capacity of the modified Artificial Bee Colony (ABC) procedure, a hybrid method is presented in which the swarm behavior of the Bacterial Foraging Optimization (BFO) algorithm is entered into the Modified Bee Colony (MBC) procedure to perform a local search. The proposed Hybrid MBC-BFO algorithm is analyzed with three different classification techniques which are separately analyzed to verify the proposed performance. The classification techniques are Artificial Neural Networks (ANN), Recursive Neural Network (ReNN), and Recurrent Neural Network (RNNs). The proposed algorithm has passed several algorithms for selecting advanced functions in terms of detection accuracy, recall, precision, false positive rate, and F-score.

Keywords


Swarm Intelligence, Modified Bee Colony, Bacterial Foraging Optimization, Feature Selection, IDS, KDDCUP’99.

References