Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks
Abstract Smart devices are enabled via the Internet of Things (IoT) and are connected in an uninterrupted world. These connected devices pose a challenge to cybersecurity systems due attacks in network communications. Such attacks have continued to threaten the operation of systems and end-users. Th...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81147-x |
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| author | Saad Said Alqahtany Asadullah Shaikh Ali Alqazzaz |
| author_facet | Saad Said Alqahtany Asadullah Shaikh Ali Alqazzaz |
| author_sort | Saad Said Alqahtany |
| collection | DOAJ |
| description | Abstract Smart devices are enabled via the Internet of Things (IoT) and are connected in an uninterrupted world. These connected devices pose a challenge to cybersecurity systems due attacks in network communications. Such attacks have continued to threaten the operation of systems and end-users. Therefore, Intrusion Detection Systems (IDS) remain one of the most used tools for maintaining such flaws against cyber-attacks. The dynamic and multi-dimensional threat landscape in IoT network increases the challenge of Traditional IDS. The focus of this paper aims to find the key features for developing an IDS that is reliable but also efficient in terms of computation. Therefore, Enhanced Grey Wolf Optimization (EGWO) for Feature Selection (FS) is implemented. The function of EGWO is to remove unnecessary features from datasets used for intrusion detection. To test the new FS technique and decide on an optimal set of features based on the accuracy achieved and the feature taking filters, the most recent FS approach relies on the NF-ToN-IoT dataset. The selected features are evaluated by using the Random Forest (RF) algorithm to combine multiple decision trees and create an accurate result. The experimental outcomes against the most recent procedures demonstrate the capacity of the recommended FS and classification methods to determine attacks in the IDS. Analysis of the results presents that the recommended approach performs more effectively than the other recent techniques with optimized features (i.e., 23 out of 43 features), high accuracy of 99.93% and improved convergence. |
| format | Article |
| id | doaj-art-52566dd180b04d96baec27dc92689aab |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-52566dd180b04d96baec27dc92689aab2025-01-19T12:18:50ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-024-81147-xEnhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networksSaad Said Alqahtany0Asadullah Shaikh1Ali Alqazzaz2Faculty of Computer and Information Systems, Islamic University of MadinahDepartment of Computer Science, College of Computer Science and Information Systems, Najran UniversityCollege of Computing and Information Technology, University of BishaAbstract Smart devices are enabled via the Internet of Things (IoT) and are connected in an uninterrupted world. These connected devices pose a challenge to cybersecurity systems due attacks in network communications. Such attacks have continued to threaten the operation of systems and end-users. Therefore, Intrusion Detection Systems (IDS) remain one of the most used tools for maintaining such flaws against cyber-attacks. The dynamic and multi-dimensional threat landscape in IoT network increases the challenge of Traditional IDS. The focus of this paper aims to find the key features for developing an IDS that is reliable but also efficient in terms of computation. Therefore, Enhanced Grey Wolf Optimization (EGWO) for Feature Selection (FS) is implemented. The function of EGWO is to remove unnecessary features from datasets used for intrusion detection. To test the new FS technique and decide on an optimal set of features based on the accuracy achieved and the feature taking filters, the most recent FS approach relies on the NF-ToN-IoT dataset. The selected features are evaluated by using the Random Forest (RF) algorithm to combine multiple decision trees and create an accurate result. The experimental outcomes against the most recent procedures demonstrate the capacity of the recommended FS and classification methods to determine attacks in the IDS. Analysis of the results presents that the recommended approach performs more effectively than the other recent techniques with optimized features (i.e., 23 out of 43 features), high accuracy of 99.93% and improved convergence.https://doi.org/10.1038/s41598-024-81147-xInternet of things (IoT) networksCyber securityIntrusion detection systemGrey Wolf OptimizationFeature selectionRandom Forest |
| spellingShingle | Saad Said Alqahtany Asadullah Shaikh Ali Alqazzaz Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks Scientific Reports Internet of things (IoT) networks Cyber security Intrusion detection system Grey Wolf Optimization Feature selection Random Forest |
| title | Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks |
| title_full | Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks |
| title_fullStr | Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks |
| title_full_unstemmed | Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks |
| title_short | Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks |
| title_sort | enhanced grey wolf optimization egwo and random forest based mechanism for intrusion detection in iot networks |
| topic | Internet of things (IoT) networks Cyber security Intrusion detection system Grey Wolf Optimization Feature selection Random Forest |
| url | https://doi.org/10.1038/s41598-024-81147-x |
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