Optimized machine learning algorithm for intrusion detection
Intrusion detection is mainly achieved by using optimization algorithms. The need for optimization algorithms for intrusion detection is necessitated by the increasing number of features in audit data, as well as the performance failure of the human-based smart intrusion detection system (IDS) in te...
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| Main Authors: | , , , , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2021-10-01.
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| Subjects: | |
| Online Access: | Get fulltext |
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| LEADER | 02679 am a22003373u 4500 | ||
|---|---|---|---|
| 001 | ijeecs25647_15599 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Alhayali, Royida A. Ibrahem |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Aljanabi, Mohammad |e author |
| 700 | 1 | 0 | |a Ali, Ahmed Hussein |e author |
| 700 | 1 | 0 | |a Mohammed, Mostafa Abdulghfoor |e author |
| 700 | 1 | 0 | |a Sutikno, Tole |e author |
| 245 | 0 | 0 | |a Optimized machine learning algorithm for intrusion detection |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-10-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25647 | ||
| 520 | |a Intrusion detection is mainly achieved by using optimization algorithms. The need for optimization algorithms for intrusion detection is necessitated by the increasing number of features in audit data, as well as the performance failure of the human-based smart intrusion detection system (IDS) in terms of their prolonged training time and classification accuracy. This article presents an improved intrusion detection technique for binary classification. The proposal is a combination of different optimizers, including Rao optimization algorithm, extreme learning machine (ELM), support vector machine (SVM), and logistic regression (LR) (for feature selection & weighting), as well as a hybrid Rao-SVM algorithm with supervised machine learning (ML) techniques for feature subset selection (FSS). The process of selecting the least number of features without sacrificing the FSS accuracy was considered a multi-objective optimization problem. The algorithm-specific, parameter-less concept of the proposed Rao-SVM was also explored in this study. The KDDCup 99 and CICIDS 2017 were used as the intrusion dataset for the experiments, where significant improvements were noted with the new Rao-SVM compared to the other algorithms. Rao-SVM presented better results than many existing works by reaching 100% accuracy for KDDCup 99 dataset and 97% for CICIDS dataset. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 24, No 1: October 2021; 590-599 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v24.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25647/15599 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25647/15599 |z Get fulltext |