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A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method

Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns...

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Autores principales: Balyan, Amit Kumar, Ahuja, Sachin, Lilhore, Umesh Kumar, Sharma, Sanjeev Kumar, Manoharan, Poongodi, Algarni, Abeer D., Elmannai, Hela, Raahemifar, Kaamran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9414798/
https://www.ncbi.nlm.nih.gov/pubmed/36015744
http://dx.doi.org/10.3390/s22165986
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author Balyan, Amit Kumar
Ahuja, Sachin
Lilhore, Umesh Kumar
Sharma, Sanjeev Kumar
Manoharan, Poongodi
Algarni, Abeer D.
Elmannai, Hela
Raahemifar, Kaamran
author_facet Balyan, Amit Kumar
Ahuja, Sachin
Lilhore, Umesh Kumar
Sharma, Sanjeev Kumar
Manoharan, Poongodi
Algarni, Abeer D.
Elmannai, Hela
Raahemifar, Kaamran
author_sort Balyan, Amit Kumar
collection PubMed
description Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns. Machine learning (ML) methods are widely used in IDS. Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. In the initial phase, the proposed HNIDS utilizes hybrid EGA-PSO methods to enhance the minor data samples and thus produce a balanced data set to learn the sample attributes of small samples more accurately. In the proposed HNIDS, a PSO method improves the vector. GA is enhanced by adding a multi-objective function, which selects the best features and achieves improved fitness outcomes to explore the essential features and helps minimize dimensions, enhance the true positive rate (TPR), and lower the false positive rate (FPR). In the next phase, an IRF eliminates the less significant attributes, incorporates a list of decision trees across each iterative process, supervises the classifier’s performance, and prevents overfitting issues. The performance of the proposed method and existing ML methods are tested using the benchmark datasets NSL-KDD. The experimental findings demonstrated that the proposed HNIDS method achieves an accuracy of 98.979% on BCC and 88.149% on MCC for the NSL-KDD dataset, which is far better than the other ML methods i.e., SVM, RF, LR, NB, LDA, and CART.
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spelling pubmed-94147982022-08-27 A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method Balyan, Amit Kumar Ahuja, Sachin Lilhore, Umesh Kumar Sharma, Sanjeev Kumar Manoharan, Poongodi Algarni, Abeer D. Elmannai, Hela Raahemifar, Kaamran Sensors (Basel) Article Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns. Machine learning (ML) methods are widely used in IDS. Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. In the initial phase, the proposed HNIDS utilizes hybrid EGA-PSO methods to enhance the minor data samples and thus produce a balanced data set to learn the sample attributes of small samples more accurately. In the proposed HNIDS, a PSO method improves the vector. GA is enhanced by adding a multi-objective function, which selects the best features and achieves improved fitness outcomes to explore the essential features and helps minimize dimensions, enhance the true positive rate (TPR), and lower the false positive rate (FPR). In the next phase, an IRF eliminates the less significant attributes, incorporates a list of decision trees across each iterative process, supervises the classifier’s performance, and prevents overfitting issues. The performance of the proposed method and existing ML methods are tested using the benchmark datasets NSL-KDD. The experimental findings demonstrated that the proposed HNIDS method achieves an accuracy of 98.979% on BCC and 88.149% on MCC for the NSL-KDD dataset, which is far better than the other ML methods i.e., SVM, RF, LR, NB, LDA, and CART. MDPI 2022-08-10 /pmc/articles/PMC9414798/ /pubmed/36015744 http://dx.doi.org/10.3390/s22165986 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Balyan, Amit Kumar
Ahuja, Sachin
Lilhore, Umesh Kumar
Sharma, Sanjeev Kumar
Manoharan, Poongodi
Algarni, Abeer D.
Elmannai, Hela
Raahemifar, Kaamran
A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
title A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
title_full A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
title_fullStr A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
title_full_unstemmed A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
title_short A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
title_sort hybrid intrusion detection model using ega-pso and improved random forest method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9414798/
https://www.ncbi.nlm.nih.gov/pubmed/36015744
http://dx.doi.org/10.3390/s22165986
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