Improved intrusion detection algorithm based on TLBO and GA algorithms
Optimization algorithms are widely used for the identification of intrusion. This is attributable to the increasing number of audit data features and the decreasing performance of human-based smart Intrusion Detection Systems (IDS) regarding classification accuracy and training time. In this paper,...
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| Format: | Article |
| Language: | English |
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Zarka Private University
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/31378/1/Improved%20intrusion%20detection%20algorithm%20based%20on%20TLBO%20and%20GA%20algorithms.pdf |
| _version_ | 1825813850210959360 |
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| author | Aljanabi, Mohammad Mohd Arfian, Ismail |
| author_facet | Aljanabi, Mohammad Mohd Arfian, Ismail |
| author_sort | Aljanabi, Mohammad |
| collection | UMP |
| description | Optimization algorithms are widely used for the identification of intrusion. This is attributable to the increasing number of audit data features and the decreasing performance of human-based smart Intrusion Detection Systems (IDS) regarding classification accuracy and training time. In this paper, an improved method for intrusion detection for binary classification was presented and discussed in detail. The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). The process of selecting the least number of features without any effect on the result accuracy in FSS was considered a multi-objective optimization problem. The NTLBO was proposed in this paper as an FSS mechanism; its algorithm-specific, parameter-less concept (which requires no parameter tuning during an optimization) was explored. The experiments were performed on the prominent intrusion machine-learning datasets (KDDCUP’99 and CICIDS 2017), where significant enhancements were observed with the suggested NTLBO algorithm as compared to the classical TeachingLearning-Based Optimization algorithm (TLBO), NTLBO presented better results than TLBO and many existing works. The results showed that NTLBO reached 100% accuracy for KDDCUP’99 dataset and 97% for CICIDS dataset. |
| first_indexed | 2024-03-06T12:50:09Z |
| format | Article |
| id | UMPir31378 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:50:09Z |
| publishDate | 2021 |
| publisher | Zarka Private University |
| record_format | dspace |
| spelling | UMPir313782021-06-29T00:34:26Z http://umpir.ump.edu.my/id/eprint/31378/ Improved intrusion detection algorithm based on TLBO and GA algorithms Aljanabi, Mohammad Mohd Arfian, Ismail QA76 Computer software Optimization algorithms are widely used for the identification of intrusion. This is attributable to the increasing number of audit data features and the decreasing performance of human-based smart Intrusion Detection Systems (IDS) regarding classification accuracy and training time. In this paper, an improved method for intrusion detection for binary classification was presented and discussed in detail. The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). The process of selecting the least number of features without any effect on the result accuracy in FSS was considered a multi-objective optimization problem. The NTLBO was proposed in this paper as an FSS mechanism; its algorithm-specific, parameter-less concept (which requires no parameter tuning during an optimization) was explored. The experiments were performed on the prominent intrusion machine-learning datasets (KDDCUP’99 and CICIDS 2017), where significant enhancements were observed with the suggested NTLBO algorithm as compared to the classical TeachingLearning-Based Optimization algorithm (TLBO), NTLBO presented better results than TLBO and many existing works. The results showed that NTLBO reached 100% accuracy for KDDCUP’99 dataset and 97% for CICIDS dataset. Zarka Private University 2021-03 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/31378/1/Improved%20intrusion%20detection%20algorithm%20based%20on%20TLBO%20and%20GA%20algorithms.pdf Aljanabi, Mohammad and Mohd Arfian, Ismail (2021) Improved intrusion detection algorithm based on TLBO and GA algorithms. International Arab Journal of Information Technology, 18 (2). pp. 170-179. ISSN 1683-3198. (Published) https://doi.org/10.34028/iajit/18/2/5 https://doi.org/10.34028/iajit/18/2/5 |
| spellingShingle | QA76 Computer software Aljanabi, Mohammad Mohd Arfian, Ismail Improved intrusion detection algorithm based on TLBO and GA algorithms |
| title | Improved intrusion detection algorithm based on TLBO and GA algorithms |
| title_full | Improved intrusion detection algorithm based on TLBO and GA algorithms |
| title_fullStr | Improved intrusion detection algorithm based on TLBO and GA algorithms |
| title_full_unstemmed | Improved intrusion detection algorithm based on TLBO and GA algorithms |
| title_short | Improved intrusion detection algorithm based on TLBO and GA algorithms |
| title_sort | improved intrusion detection algorithm based on tlbo and ga algorithms |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/31378/1/Improved%20intrusion%20detection%20algorithm%20based%20on%20TLBO%20and%20GA%20algorithms.pdf |
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