A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
<p dir="ltr">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 suspiciou...
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2022
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| _version_ | 1864513516859293696 |
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| author | Amit Kumar Balyan (18288964) |
| author2 | Sachin Ahuja (13903010) Umesh Kumar Lilhore (17727684) Sanjeev Kumar Sharma (5463875) Poongodi Manoharan (17727687) Abeer D. Algarni (18288967) Hela Elmannai (18288970) Kaamran Raahemifar (707645) |
| author2_role | author author author author author author author |
| author_facet | Amit Kumar Balyan (18288964) Sachin Ahuja (13903010) Umesh Kumar Lilhore (17727684) Sanjeev Kumar Sharma (5463875) Poongodi Manoharan (17727687) Abeer D. Algarni (18288967) Hela Elmannai (18288970) Kaamran Raahemifar (707645) |
| author_role | author |
| dc.creator.none.fl_str_mv | Amit Kumar Balyan (18288964) Sachin Ahuja (13903010) Umesh Kumar Lilhore (17727684) Sanjeev Kumar Sharma (5463875) Poongodi Manoharan (17727687) Abeer D. Algarni (18288967) Hela Elmannai (18288970) Kaamran Raahemifar (707645) |
| dc.date.none.fl_str_mv | 2022-08-10T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s22165986 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Hybrid_Intrusion_Detection_Model_Using_EGA-PSO_and_Improved_Random_Forest_Method/25524052 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Cybersecurity and privacy Machine learning Hybrid IDS genetic algorithm particle swarm optimization random forest machine learning intrusion detection security |
| dc.title.none.fl_str_mv | A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s22165986" target="_blank">https://dx.doi.org/10.3390/s22165986</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_3e5815069134dc377f78d27b9689d048 |
| identifier_str_mv | 10.3390/s22165986 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25524052 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest MethodAmit Kumar Balyan (18288964)Sachin Ahuja (13903010)Umesh Kumar Lilhore (17727684)Sanjeev Kumar Sharma (5463875)Poongodi Manoharan (17727687)Abeer D. Algarni (18288967)Hela Elmannai (18288970)Kaamran Raahemifar (707645)Information and computing sciencesCybersecurity and privacyMachine learningHybrid IDSgenetic algorithmparticle swarm optimizationrandom forestmachine learningintrusion detectionsecurity<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s22165986" target="_blank">https://dx.doi.org/10.3390/s22165986</a></p>2022-08-10T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s22165986https://figshare.com/articles/journal_contribution/A_Hybrid_Intrusion_Detection_Model_Using_EGA-PSO_and_Improved_Random_Forest_Method/25524052CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/255240522022-08-10T03:00:00Z |
| spellingShingle | A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method Amit Kumar Balyan (18288964) Information and computing sciences Cybersecurity and privacy Machine learning Hybrid IDS genetic algorithm particle swarm optimization random forest machine learning intrusion detection security |
| status_str | publishedVersion |
| 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 | A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method |
| topic | Information and computing sciences Cybersecurity and privacy Machine learning Hybrid IDS genetic algorithm particle swarm optimization random forest machine learning intrusion detection security |