An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security
Abstract In recent times, there has been rapid growth of technologies that have enabled smart infrastructures-IoT-powered smart grids, cities, and healthcare systems. But these resource-constrained IoT devices cannot be protected by existing security mechanisms against emerging cyber threats. The ai...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-84691-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841544809560408064 |
|---|---|
| author | J. Maruthupandi S. Sivakumar B. Lakshmi Dhevi S. Prasanna R. Karpaga Priya Shitharth Selvarajan |
| author_facet | J. Maruthupandi S. Sivakumar B. Lakshmi Dhevi S. Prasanna R. Karpaga Priya Shitharth Selvarajan |
| author_sort | J. Maruthupandi |
| collection | DOAJ |
| description | Abstract In recent times, there has been rapid growth of technologies that have enabled smart infrastructures-IoT-powered smart grids, cities, and healthcare systems. But these resource-constrained IoT devices cannot be protected by existing security mechanisms against emerging cyber threats. The aim of the paper is to present an improved security for smart healthcare IoT systems by developing an architecture for IADCL. The proposed system employs publicly available datasets such as CIC-IDS 2017, CIC-IDS 2018, CIC-Bell DNS 2021, and NSL-KDD to present a robust detection framework. IRKO selects features, reducing the feature dimensions and hence isolating the most relevant attributes. The AConBN classifier then accurately classifies normal and intrusion traffic. Afterwards, optimization in the classification process is done by the SA-HHO algorithm, which provides the optimal weight values. Results are such that the IADCL framework detects cyberattacks with a high degree of accuracy, and the performance evaluations are made based on a number of key performance metrics. Conclusively, the proposed system has very good potential to protect smart healthcare IoT devices from cyber threats. |
| format | Article |
| id | doaj-art-39a72c4c38954d08a34a6023e66d5fbc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-39a72c4c38954d08a34a6023e66d5fbc2025-01-12T12:16:22ZengNature PortfolioScientific Reports2045-23222025-01-0115112110.1038/s41598-024-84691-8An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare securityJ. Maruthupandi0S. Sivakumar1B. Lakshmi Dhevi2S. Prasanna3R. Karpaga Priya4Shitharth Selvarajan5Department of Computer Science and Engineering, New Horizon College of EngineeringDepartment of Computer Science and Engineering, SRM Institute of Science and TechnologyDepartment of Networking and Communications, SRM Institute of Science and TechnologySchool of Computer Science Engineering and Information Systems, Vellore Institute of TechnologyDepartment of Electrical and Electronics Engineering, Saveetha Engineering CollegeDepartment of Computer Science and Engineering, Kebri Dehar UniversityAbstract In recent times, there has been rapid growth of technologies that have enabled smart infrastructures-IoT-powered smart grids, cities, and healthcare systems. But these resource-constrained IoT devices cannot be protected by existing security mechanisms against emerging cyber threats. The aim of the paper is to present an improved security for smart healthcare IoT systems by developing an architecture for IADCL. The proposed system employs publicly available datasets such as CIC-IDS 2017, CIC-IDS 2018, CIC-Bell DNS 2021, and NSL-KDD to present a robust detection framework. IRKO selects features, reducing the feature dimensions and hence isolating the most relevant attributes. The AConBN classifier then accurately classifies normal and intrusion traffic. Afterwards, optimization in the classification process is done by the SA-HHO algorithm, which provides the optimal weight values. Results are such that the IADCL framework detects cyberattacks with a high degree of accuracy, and the performance evaluations are made based on a number of key performance metrics. Conclusively, the proposed system has very good potential to protect smart healthcare IoT devices from cyber threats.https://doi.org/10.1038/s41598-024-84691-8Smart healthcareSecurityArtificial Intelligence (AI)Intrusion detectionIntelligent Attention based Deep Convoluted Learning (IADCL)Optimization |
| spellingShingle | J. Maruthupandi S. Sivakumar B. Lakshmi Dhevi S. Prasanna R. Karpaga Priya Shitharth Selvarajan An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security Scientific Reports Smart healthcare Security Artificial Intelligence (AI) Intrusion detection Intelligent Attention based Deep Convoluted Learning (IADCL) Optimization |
| title | An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security |
| title_full | An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security |
| title_fullStr | An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security |
| title_full_unstemmed | An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security |
| title_short | An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security |
| title_sort | intelligent attention based deep convoluted learning iadcl model for smart healthcare security |
| topic | Smart healthcare Security Artificial Intelligence (AI) Intrusion detection Intelligent Attention based Deep Convoluted Learning (IADCL) Optimization |
| url | https://doi.org/10.1038/s41598-024-84691-8 |
| work_keys_str_mv | AT jmaruthupandi anintelligentattentionbaseddeepconvolutedlearningiadclmodelforsmarthealthcaresecurity AT ssivakumar anintelligentattentionbaseddeepconvolutedlearningiadclmodelforsmarthealthcaresecurity AT blakshmidhevi anintelligentattentionbaseddeepconvolutedlearningiadclmodelforsmarthealthcaresecurity AT sprasanna anintelligentattentionbaseddeepconvolutedlearningiadclmodelforsmarthealthcaresecurity AT rkarpagapriya anintelligentattentionbaseddeepconvolutedlearningiadclmodelforsmarthealthcaresecurity AT shitharthselvarajan anintelligentattentionbaseddeepconvolutedlearningiadclmodelforsmarthealthcaresecurity AT jmaruthupandi intelligentattentionbaseddeepconvolutedlearningiadclmodelforsmarthealthcaresecurity AT ssivakumar intelligentattentionbaseddeepconvolutedlearningiadclmodelforsmarthealthcaresecurity AT blakshmidhevi intelligentattentionbaseddeepconvolutedlearningiadclmodelforsmarthealthcaresecurity AT sprasanna intelligentattentionbaseddeepconvolutedlearningiadclmodelforsmarthealthcaresecurity AT rkarpagapriya intelligentattentionbaseddeepconvolutedlearningiadclmodelforsmarthealthcaresecurity AT shitharthselvarajan intelligentattentionbaseddeepconvolutedlearningiadclmodelforsmarthealthcaresecurity |