Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm
Abstract The rapid expansion of the Internet of Things (IoT) has significantly improved the various aspects of our daily life. However, along with its benefits, new security threats such as Denial of Service (DoS) attacks and Botnets have emerged. To adopt this technology and integrity of IoT enviro...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-99574-9 |
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| author | Amit Sagu Nasib Singh Gill Preeti Gulia Noha Alduaiji Piyush Kumar Shukla Mohd Asif Shah |
| author_facet | Amit Sagu Nasib Singh Gill Preeti Gulia Noha Alduaiji Piyush Kumar Shukla Mohd Asif Shah |
| author_sort | Amit Sagu |
| collection | DOAJ |
| description | Abstract The rapid expansion of the Internet of Things (IoT) has significantly improved the various aspects of our daily life. However, along with its benefits, new security threats such as Denial of Service (DoS) attacks and Botnets have emerged. To adopt this technology and integrity of IoT environment, detection of such attacks become crucial. This paper proposes a hybrid deep learning model that combines Convolutional Neural Network (CNN) and Gated Recurrent Units (GRUs) to classify the IoT security threats. The CNN is used to extract the spatial features from the network data, where on the other hand GRUs used for capturing the temporal dependencies. This combination makes the model effective at analysing both static and dynamic aspects of network data. Further, to optimize the performance of the proposed hybrid model, self-upgraded Cat and Mouse Optimization (SUCMO) algorithm is employed, a state of art optimization technique. The SUCMO algorithm fine-tunes the deep learning model’s hyperparameters to improve classification accuracy. The proposed model is evaluated through experiments on two different datasets i.e., UNSW-NB15 and BoT-IoT, and results demonstrates that proposed work outperforms the traditional work as well as state of the art works. |
| format | Article |
| id | doaj-art-797a7b01fed640d6a4b7ec2a2a5926c3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-797a7b01fed640d6a4b7ec2a2a5926c32025-08-20T03:53:46ZengNature PortfolioScientific Reports2045-23222025-05-0115111610.1038/s41598-025-99574-9Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithmAmit Sagu0Nasib Singh Gill1Preeti Gulia2Noha Alduaiji3Piyush Kumar Shukla4Mohd Asif Shah5Department of Computer Science and Applications, Maharshi Dayanand UniversityDepartment of Computer Science and Applications, Maharshi Dayanand UniversityDepartment of Computer Science and Applications, Maharshi Dayanand UniversityDepartment of Computer Science, College of Computer and Information Sciences, Majmaah UniversityDepartment of Computer Science & Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (State Technological University of Madhya Pradesh)Department of Economics, Kardan UniversityAbstract The rapid expansion of the Internet of Things (IoT) has significantly improved the various aspects of our daily life. However, along with its benefits, new security threats such as Denial of Service (DoS) attacks and Botnets have emerged. To adopt this technology and integrity of IoT environment, detection of such attacks become crucial. This paper proposes a hybrid deep learning model that combines Convolutional Neural Network (CNN) and Gated Recurrent Units (GRUs) to classify the IoT security threats. The CNN is used to extract the spatial features from the network data, where on the other hand GRUs used for capturing the temporal dependencies. This combination makes the model effective at analysing both static and dynamic aspects of network data. Further, to optimize the performance of the proposed hybrid model, self-upgraded Cat and Mouse Optimization (SUCMO) algorithm is employed, a state of art optimization technique. The SUCMO algorithm fine-tunes the deep learning model’s hyperparameters to improve classification accuracy. The proposed model is evaluated through experiments on two different datasets i.e., UNSW-NB15 and BoT-IoT, and results demonstrates that proposed work outperforms the traditional work as well as state of the art works.https://doi.org/10.1038/s41598-025-99574-9IoT attacks classificationDeep learningIDSHybrid model |
| spellingShingle | Amit Sagu Nasib Singh Gill Preeti Gulia Noha Alduaiji Piyush Kumar Shukla Mohd Asif Shah Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm Scientific Reports IoT attacks classification Deep learning IDS Hybrid model |
| title | Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm |
| title_full | Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm |
| title_fullStr | Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm |
| title_full_unstemmed | Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm |
| title_short | Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm |
| title_sort | advances to iot security using a gru cnn deep learning model trained on sucmo algorithm |
| topic | IoT attacks classification Deep learning IDS Hybrid model |
| url | https://doi.org/10.1038/s41598-025-99574-9 |
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