Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System
Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. This condition is attributed to the increasing number of audit data features and the decreasing performance of human-based smart intrusion detection systems regarding classifi...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi
2020
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30640/1/Improved%20TLBO-JAYA%20Algorithm.pdf |
| _version_ | 1796994470725550080 |
|---|---|
| author | Aljanabi, Mohammad Mohd Arfian, Ismail Mezhuyev, Vitaliy |
| author_facet | Aljanabi, Mohammad Mohd Arfian, Ismail Mezhuyev, Vitaliy |
| author_sort | Aljanabi, Mohammad |
| collection | UMP |
| description | Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. This condition is attributed to the increasing number of audit data features and the decreasing performance of human-based smart intrusion detection systems regarding classification accuracy, false alarm rate, and classification time. Feature selection and classifier parameter tuning are important factors that affect the performance of any intrusion detection system. In this paper, an improved intrusion detection algorithm for multiclass classification was presented and discussed in detail. The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). The selection of the least number of features without causing an effect on the result accuracy in FSS is a multiobjective optimisation problem. This work proposes ITLBO as an FSS mechanism, and its algorithm-specific, parameterless concept (no parameter tuning is required during optimisation) was explored. IPJAYA in this study was used to update the C and gamma parameters of the support vector machine (SVM). Several experiments were performed on the prominent intrusion ML dataset, where significant enhancements were observed with the suggested ITLBO-IPJAYA-SVM algorithm compared with the classical TLBO and JAYA algorithms. |
| first_indexed | 2024-03-06T12:48:13Z |
| format | Article |
| id | UMPir30640 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:48:13Z |
| publishDate | 2020 |
| publisher | Hindawi |
| record_format | dspace |
| spelling | UMPir306402021-02-05T02:41:14Z http://umpir.ump.edu.my/id/eprint/30640/ Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System Aljanabi, Mohammad Mohd Arfian, Ismail Mezhuyev, Vitaliy QA75 Electronic computers. Computer science Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. This condition is attributed to the increasing number of audit data features and the decreasing performance of human-based smart intrusion detection systems regarding classification accuracy, false alarm rate, and classification time. Feature selection and classifier parameter tuning are important factors that affect the performance of any intrusion detection system. In this paper, an improved intrusion detection algorithm for multiclass classification was presented and discussed in detail. The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). The selection of the least number of features without causing an effect on the result accuracy in FSS is a multiobjective optimisation problem. This work proposes ITLBO as an FSS mechanism, and its algorithm-specific, parameterless concept (no parameter tuning is required during optimisation) was explored. IPJAYA in this study was used to update the C and gamma parameters of the support vector machine (SVM). Several experiments were performed on the prominent intrusion ML dataset, where significant enhancements were observed with the suggested ITLBO-IPJAYA-SVM algorithm compared with the classical TLBO and JAYA algorithms. Hindawi 2020 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/30640/1/Improved%20TLBO-JAYA%20Algorithm.pdf Aljanabi, Mohammad and Mohd Arfian, Ismail and Mezhuyev, Vitaliy (2020) Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System. Complexity, 2020 (287684). pp. 1-18. ISSN 1099-0526 (Online). (Published) https://doi.org/10.1155/2020/5287684 https://doi.org/10.1155/2020/5287684 |
| spellingShingle | QA75 Electronic computers. Computer science Aljanabi, Mohammad Mohd Arfian, Ismail Mezhuyev, Vitaliy Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System |
| title | Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System |
| title_full | Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System |
| title_fullStr | Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System |
| title_full_unstemmed | Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System |
| title_short | Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System |
| title_sort | improved tlbo jaya algorithm for subset feature selection and parameter optimisation in intrusion detection system |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/30640/1/Improved%20TLBO-JAYA%20Algorithm.pdf |
| work_keys_str_mv | AT aljanabimohammad improvedtlbojayaalgorithmforsubsetfeatureselectionandparameteroptimisationinintrusiondetectionsystem AT mohdarfianismail improvedtlbojayaalgorithmforsubsetfeatureselectionandparameteroptimisationinintrusiondetectionsystem AT mezhuyevvitaliy improvedtlbojayaalgorithmforsubsetfeatureselectionandparameteroptimisationinintrusiondetectionsystem |