A novel lightweight deep learning framework using enhanced pelican optimization for efficient cyberattack detection in the Internet of Things environments
Abstract The extensive use of Internet of Things (IoT) technology produces unprecedented connectivity and cyberattack exposure. Recent attack detection tools have poor accuracy, efficiency, and adaptability in the case of IoT systems with scarce resources. To counter these challenges, the current st...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-06-01
|
| Series: | Journal of Engineering and Applied Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s44147-025-00635-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850138036450361344 |
|---|---|
| author | Yaozhi Chen Yan Guo Yun Gao Baozhong Liu |
| author_facet | Yaozhi Chen Yan Guo Yun Gao Baozhong Liu |
| author_sort | Yaozhi Chen |
| collection | DOAJ |
| description | Abstract The extensive use of Internet of Things (IoT) technology produces unprecedented connectivity and cyberattack exposure. Recent attack detection tools have poor accuracy, efficiency, and adaptability in the case of IoT systems with scarce resources. To counter these challenges, the current study proposes a hybrid model incorporating an efficient convolutional neural network (CNN) and an enhanced pelican optimization algorithm (EPOA) to detect IoT network attacks. Inspired by how pelicans hunt, EPOA maximizes CNN’s hyperparameters and feature selection for higher accuracy and efficiency in computation. Experimentation with the Bot-IoT, CICIDS2018, and NSL-KDD datasets validates the performance of the proposed EPOA-based deep learning method for cyberattack detection. The model achieves 98.1% accuracy on Bot-IoT, 97.4% on NSL-KDD, and 97.9% on CICIDS2018, outperforming conventional approaches like long short-term memory (LSTM), gated recurrent unit (GRU), support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and recurrent neural network (RNN). The model also produces a minimum loss value of 0.17, outperforming other approaches with the shortest execution duration. With its efficient design and high detection performance, the proposed approach is highly suitable for continuous IoT cyberattack detection in practical deployment scenarios. |
| format | Article |
| id | doaj-art-01a5f1ead05b475a8a0da8dd4a02affe |
| institution | OA Journals |
| issn | 1110-1903 2536-9512 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Engineering and Applied Science |
| spelling | doaj-art-01a5f1ead05b475a8a0da8dd4a02affe2025-08-20T02:30:41ZengSpringerOpenJournal of Engineering and Applied Science1110-19032536-95122025-06-0172112610.1186/s44147-025-00635-7A novel lightweight deep learning framework using enhanced pelican optimization for efficient cyberattack detection in the Internet of Things environmentsYaozhi Chen0Yan Guo1Yun Gao2Baozhong Liu3Sichuan Vocational and Technical CollegeSichuan Vocational and Technical CollegeSichuan Vocational and Technical CollegeSichuan Vocational and Technical CollegeAbstract The extensive use of Internet of Things (IoT) technology produces unprecedented connectivity and cyberattack exposure. Recent attack detection tools have poor accuracy, efficiency, and adaptability in the case of IoT systems with scarce resources. To counter these challenges, the current study proposes a hybrid model incorporating an efficient convolutional neural network (CNN) and an enhanced pelican optimization algorithm (EPOA) to detect IoT network attacks. Inspired by how pelicans hunt, EPOA maximizes CNN’s hyperparameters and feature selection for higher accuracy and efficiency in computation. Experimentation with the Bot-IoT, CICIDS2018, and NSL-KDD datasets validates the performance of the proposed EPOA-based deep learning method for cyberattack detection. The model achieves 98.1% accuracy on Bot-IoT, 97.4% on NSL-KDD, and 97.9% on CICIDS2018, outperforming conventional approaches like long short-term memory (LSTM), gated recurrent unit (GRU), support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and recurrent neural network (RNN). The model also produces a minimum loss value of 0.17, outperforming other approaches with the shortest execution duration. With its efficient design and high detection performance, the proposed approach is highly suitable for continuous IoT cyberattack detection in practical deployment scenarios.https://doi.org/10.1186/s44147-025-00635-7Internet of ThingsCyberattackDeep learningPelican algorithmOptimization |
| spellingShingle | Yaozhi Chen Yan Guo Yun Gao Baozhong Liu A novel lightweight deep learning framework using enhanced pelican optimization for efficient cyberattack detection in the Internet of Things environments Journal of Engineering and Applied Science Internet of Things Cyberattack Deep learning Pelican algorithm Optimization |
| title | A novel lightweight deep learning framework using enhanced pelican optimization for efficient cyberattack detection in the Internet of Things environments |
| title_full | A novel lightweight deep learning framework using enhanced pelican optimization for efficient cyberattack detection in the Internet of Things environments |
| title_fullStr | A novel lightweight deep learning framework using enhanced pelican optimization for efficient cyberattack detection in the Internet of Things environments |
| title_full_unstemmed | A novel lightweight deep learning framework using enhanced pelican optimization for efficient cyberattack detection in the Internet of Things environments |
| title_short | A novel lightweight deep learning framework using enhanced pelican optimization for efficient cyberattack detection in the Internet of Things environments |
| title_sort | novel lightweight deep learning framework using enhanced pelican optimization for efficient cyberattack detection in the internet of things environments |
| topic | Internet of Things Cyberattack Deep learning Pelican algorithm Optimization |
| url | https://doi.org/10.1186/s44147-025-00635-7 |
| work_keys_str_mv | AT yaozhichen anovellightweightdeeplearningframeworkusingenhancedpelicanoptimizationforefficientcyberattackdetectionintheinternetofthingsenvironments AT yanguo anovellightweightdeeplearningframeworkusingenhancedpelicanoptimizationforefficientcyberattackdetectionintheinternetofthingsenvironments AT yungao anovellightweightdeeplearningframeworkusingenhancedpelicanoptimizationforefficientcyberattackdetectionintheinternetofthingsenvironments AT baozhongliu anovellightweightdeeplearningframeworkusingenhancedpelicanoptimizationforefficientcyberattackdetectionintheinternetofthingsenvironments AT yaozhichen novellightweightdeeplearningframeworkusingenhancedpelicanoptimizationforefficientcyberattackdetectionintheinternetofthingsenvironments AT yanguo novellightweightdeeplearningframeworkusingenhancedpelicanoptimizationforefficientcyberattackdetectionintheinternetofthingsenvironments AT yungao novellightweightdeeplearningframeworkusingenhancedpelicanoptimizationforefficientcyberattackdetectionintheinternetofthingsenvironments AT baozhongliu novellightweightdeeplearningframeworkusingenhancedpelicanoptimizationforefficientcyberattackdetectionintheinternetofthingsenvironments |