A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security
Abstract Adversarial attacks were commonly considered in computer vision (CV), but their effect on network security apps rests in the field of open investigation. As IoT, AI, and 5G endure to unite and understand the potential of Industry 4.0, security events and incidents on IoT systems have been e...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-85878-3 |
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| author | Kashi Sai Prasad P Udayakumar E. Laxmi Lydia Mohammed Altaf Ahmed Mohamad Khairi Ishak Faten Khalid Karim Samih M. Mostafa |
| author_facet | Kashi Sai Prasad P Udayakumar E. Laxmi Lydia Mohammed Altaf Ahmed Mohamad Khairi Ishak Faten Khalid Karim Samih M. Mostafa |
| author_sort | Kashi Sai Prasad |
| collection | DOAJ |
| description | Abstract Adversarial attacks were commonly considered in computer vision (CV), but their effect on network security apps rests in the field of open investigation. As IoT, AI, and 5G endure to unite and understand the potential of Industry 4.0, security events and incidents on IoT systems have been enlarged. While IoT networks efficiently deliver intellectual services, the vast amount of data processed and collected in IoT networks also creates severe security concerns. Numerous research works were keen to project intelligent network intrusion detection systems (NIDS) to avert the exploitation of IoT data through smart applications. Deep learning (DL) models are applied to perceive and alleviate numerous security attacks against IoT networks. DL has a considerable reputation in NIDS, owing to its robust ability to identify delicate differences between malicious and normal network activities. While a diversity of models are aimed at influencing DL techniques for security protection, whether these methods are exposed to adversarial examples is unidentified. This study introduces a Two-Tier Optimization Strategy for Robust Adversarial Attack Mitigation in (TTOS-RAAM) model for IoT network security. The major aim of the TTOS-RAAM technique is to recognize the presence of adversarial attack behaviour in the IoT. Primarily, the TTOS-RAAM technique utilizes a min-max scaler to scale the input data into a uniform format. Besides, a hybrid of the coati–grey wolf optimization (CGWO) approach is utilized for optimum feature selection. Moreover, the TTOS-RAAM technique employs the conditional variational autoencoder (CVAE) technique to detect adversarial attacks. Finally, the parameter adjustment of the CVAE model is performed by utilizing an improved chaos African vulture optimization (ICAVO) model. A wide range of experimentation analyses is performed and the outcomes are observed under numerous aspects using the RT-IoT2022 dataset. The performance validation of the TTOS-RAAM technique portrayed a superior accuracy value of 99.91% over existing approaches. |
| format | Article |
| id | doaj-art-bfbe62221aa140dd88138ca27bdb3bd9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bfbe62221aa140dd88138ca27bdb3bd92025-01-19T12:22:33ZengNature PortfolioScientific Reports2045-23222025-01-0115113010.1038/s41598-025-85878-3A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network securityKashi Sai Prasad0P Udayakumar1E. Laxmi Lydia2Mohammed Altaf Ahmed3Mohamad Khairi Ishak4Faten Khalid Karim5Samih M. Mostafa6Department of CSE-AI&ML, MLR Institute of TechnologyDepartment of Computer Science and Engineering, Akshaya College of Engineering and TechnologyDepartment of Information Technology, VR Siddhartha Engineering College(A), Siddhartha Academy of Higher Education (Deemed to be University)Department of Computer Engineering, College of Computer Engineering & Sciences, Prince Sattam Bin Abdulaziz UniversityDepartment of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman UniversityDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityComputer Science Department, Faculty of Computers and Information, South Valley UniversityAbstract Adversarial attacks were commonly considered in computer vision (CV), but their effect on network security apps rests in the field of open investigation. As IoT, AI, and 5G endure to unite and understand the potential of Industry 4.0, security events and incidents on IoT systems have been enlarged. While IoT networks efficiently deliver intellectual services, the vast amount of data processed and collected in IoT networks also creates severe security concerns. Numerous research works were keen to project intelligent network intrusion detection systems (NIDS) to avert the exploitation of IoT data through smart applications. Deep learning (DL) models are applied to perceive and alleviate numerous security attacks against IoT networks. DL has a considerable reputation in NIDS, owing to its robust ability to identify delicate differences between malicious and normal network activities. While a diversity of models are aimed at influencing DL techniques for security protection, whether these methods are exposed to adversarial examples is unidentified. This study introduces a Two-Tier Optimization Strategy for Robust Adversarial Attack Mitigation in (TTOS-RAAM) model for IoT network security. The major aim of the TTOS-RAAM technique is to recognize the presence of adversarial attack behaviour in the IoT. Primarily, the TTOS-RAAM technique utilizes a min-max scaler to scale the input data into a uniform format. Besides, a hybrid of the coati–grey wolf optimization (CGWO) approach is utilized for optimum feature selection. Moreover, the TTOS-RAAM technique employs the conditional variational autoencoder (CVAE) technique to detect adversarial attacks. Finally, the parameter adjustment of the CVAE model is performed by utilizing an improved chaos African vulture optimization (ICAVO) model. A wide range of experimentation analyses is performed and the outcomes are observed under numerous aspects using the RT-IoT2022 dataset. The performance validation of the TTOS-RAAM technique portrayed a superior accuracy value of 99.91% over existing approaches.https://doi.org/10.1038/s41598-025-85878-3Two-tier optimizationAdversarial attackIoTIntrusion detection systemHybrid feature selectionAfrican vulture optimization |
| spellingShingle | Kashi Sai Prasad P Udayakumar E. Laxmi Lydia Mohammed Altaf Ahmed Mohamad Khairi Ishak Faten Khalid Karim Samih M. Mostafa A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security Scientific Reports Two-tier optimization Adversarial attack IoT Intrusion detection system Hybrid feature selection African vulture optimization |
| title | A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security |
| title_full | A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security |
| title_fullStr | A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security |
| title_full_unstemmed | A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security |
| title_short | A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security |
| title_sort | two tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security |
| topic | Two-tier optimization Adversarial attack IoT Intrusion detection system Hybrid feature selection African vulture optimization |
| url | https://doi.org/10.1038/s41598-025-85878-3 |
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