Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest
Abstract Intrusion detection in Wireless Sensor Networks (WSNs) is an emerging area of research, given their extensive use in sensitive fields like military surveillance, healthcare, environmental monitoring, and smart cities. However, WSNs face several security challenges due to their limited compu...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-03498-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850243445624406016 |
|---|---|
| author | Vivek Kumar Pandey Shiv Prakash Tarun Kumar Gupta Priyanshu Sinha Tiansheng Yang Rajkumar Singh Rathore Lu Wang Sabeen Tahir Sheikh Tahir Bakhsh |
| author_facet | Vivek Kumar Pandey Shiv Prakash Tarun Kumar Gupta Priyanshu Sinha Tiansheng Yang Rajkumar Singh Rathore Lu Wang Sabeen Tahir Sheikh Tahir Bakhsh |
| author_sort | Vivek Kumar Pandey |
| collection | DOAJ |
| description | Abstract Intrusion detection in Wireless Sensor Networks (WSNs) is an emerging area of research, given their extensive use in sensitive fields like military surveillance, healthcare, environmental monitoring, and smart cities. However, WSNs face several security challenges due to their limited computational capabilities and energy constraints. Their deployment in open, unattended environments makes them especially vulnerable to threats like eavesdropping, interference, and jamming. To address this problem, Random Forest (RF) is a popular machine learning model. The RF model can be tweaked because of its multiple hyperparameters. Tuning these parameters manually is tedious, as the combinations will be exponential. This work presents an enhanced intrusion detection approach by integrating Tabu Search (TS) optimization with a RF classifier. As a result, TS will help RF automatically search optimal hyperparameters and improve the generalization ability. This work integrates the pros of TS with RF. The model was tested on three different datasets, i.e., (a) the WSN-DS dataset, (b) CICIDS 2017, and (c) the CIC-IoT 2023 dataset, which shows better results on different metrics like precision, recall, F1-score, Cohen’s Kappa, and ROC AUC. Detection of Blackhole and Gray Hole attacks also improved, demonstrating the effectiveness of combining metaheuristic optimization with ensemble learning for stronger WSN security. |
| format | Article |
| id | doaj-art-b25f69a5fd6541b49f2a7939c3cdc9d2 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b25f69a5fd6541b49f2a7939c3cdc9d22025-08-20T02:00:00ZengNature PortfolioScientific Reports2045-23222025-05-0115112110.1038/s41598-025-03498-3Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forestVivek Kumar Pandey0Shiv Prakash1Tarun Kumar Gupta2Priyanshu Sinha3Tiansheng Yang4Rajkumar Singh Rathore5Lu Wang6Sabeen Tahir7Sheikh Tahir Bakhsh8Department of Electronics and Communication, University of AllahabadDepartment of Electronics and Communication, University of AllahabadDepartment of Computer Science, Miranda House, University of DelhiDepartment of Electronics and Communication, University of AllahabadUniversity of South Wales, Llantwit RdDepartment of Computer Science, Cardiff School of Technologies, Cardiff Metropolitan UniversityXi’an Jiaotong-Liverpool UniversityDepartment of Computer Science, Cardiff School of Technologies, Cardiff Metropolitan UniversityDepartment of Computer Science, Cardiff School of Technologies, Cardiff Metropolitan UniversityAbstract Intrusion detection in Wireless Sensor Networks (WSNs) is an emerging area of research, given their extensive use in sensitive fields like military surveillance, healthcare, environmental monitoring, and smart cities. However, WSNs face several security challenges due to their limited computational capabilities and energy constraints. Their deployment in open, unattended environments makes them especially vulnerable to threats like eavesdropping, interference, and jamming. To address this problem, Random Forest (RF) is a popular machine learning model. The RF model can be tweaked because of its multiple hyperparameters. Tuning these parameters manually is tedious, as the combinations will be exponential. This work presents an enhanced intrusion detection approach by integrating Tabu Search (TS) optimization with a RF classifier. As a result, TS will help RF automatically search optimal hyperparameters and improve the generalization ability. This work integrates the pros of TS with RF. The model was tested on three different datasets, i.e., (a) the WSN-DS dataset, (b) CICIDS 2017, and (c) the CIC-IoT 2023 dataset, which shows better results on different metrics like precision, recall, F1-score, Cohen’s Kappa, and ROC AUC. Detection of Blackhole and Gray Hole attacks also improved, demonstrating the effectiveness of combining metaheuristic optimization with ensemble learning for stronger WSN security.https://doi.org/10.1038/s41598-025-03498-3Wireless sensor networks (WSNs)Intrusion detectionWSN-DSTabu searchRandom forestOptimization |
| spellingShingle | Vivek Kumar Pandey Shiv Prakash Tarun Kumar Gupta Priyanshu Sinha Tiansheng Yang Rajkumar Singh Rathore Lu Wang Sabeen Tahir Sheikh Tahir Bakhsh Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest Scientific Reports Wireless sensor networks (WSNs) Intrusion detection WSN-DS Tabu search Random forest Optimization |
| title | Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest |
| title_full | Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest |
| title_fullStr | Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest |
| title_full_unstemmed | Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest |
| title_short | Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest |
| title_sort | enhancing intrusion detection in wireless sensor networks using a tabu search based optimized random forest |
| topic | Wireless sensor networks (WSNs) Intrusion detection WSN-DS Tabu search Random forest Optimization |
| url | https://doi.org/10.1038/s41598-025-03498-3 |
| work_keys_str_mv | AT vivekkumarpandey enhancingintrusiondetectioninwirelesssensornetworksusingatabusearchbasedoptimizedrandomforest AT shivprakash enhancingintrusiondetectioninwirelesssensornetworksusingatabusearchbasedoptimizedrandomforest AT tarunkumargupta enhancingintrusiondetectioninwirelesssensornetworksusingatabusearchbasedoptimizedrandomforest AT priyanshusinha enhancingintrusiondetectioninwirelesssensornetworksusingatabusearchbasedoptimizedrandomforest AT tianshengyang enhancingintrusiondetectioninwirelesssensornetworksusingatabusearchbasedoptimizedrandomforest AT rajkumarsinghrathore enhancingintrusiondetectioninwirelesssensornetworksusingatabusearchbasedoptimizedrandomforest AT luwang enhancingintrusiondetectioninwirelesssensornetworksusingatabusearchbasedoptimizedrandomforest AT sabeentahir enhancingintrusiondetectioninwirelesssensornetworksusingatabusearchbasedoptimizedrandomforest AT sheikhtahirbakhsh enhancingintrusiondetectioninwirelesssensornetworksusingatabusearchbasedoptimizedrandomforest |