Improving the Security of Internet of Things (IoT) Applications Based on a New Machine Learning Technique
The Internet of Things, or IoT, is changing practically every aspect of modern life and entering both the business and residential domains. The proliferation of IoT has led to a rise in the frequency of cyber catastrophes. Attackers are using new methods or changing old ones, making the danger more...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00164.pdf |
| _version_ | 1827287317322137600 |
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| author | Hussein Alia A. Ramadhan Ali J. TaeiZadeh Ali Hussein Issa Mohand |
| author_facet | Hussein Alia A. Ramadhan Ali J. TaeiZadeh Ali Hussein Issa Mohand |
| author_sort | Hussein Alia A. |
| collection | DOAJ |
| description | The Internet of Things, or IoT, is changing practically every aspect of modern life and entering both the business and residential domains. The proliferation of IoT has led to a rise in the frequency of cyber catastrophes. Attackers are using new methods or changing old ones, making the danger more sophisticated. The threat landscape that security professionals face is dynamic, complex, and diversified. This paper proposes a novel approach to enhance Internet of Things applications by fusing the swarm intelligence of Salp Swarm Algorithms (SSA) with the predictive power of Random Forest (RF) and Decision Tree (DT) models. Salp Swarm Algorithms simulate the cooperative behavior of salps in the natural world, wherein individual agents coordinate their actions to achieve common goals. This work uses SSA to optimize the Random Forest and Decision Tree model training process in an IoT context. SSA's collaborative nature makes it easier to explore the solution space effectively, which enhances the models' ability to capture the complex correlations found in IoT data. The effectiveness of the model is evaluated. We were able to attain a maximum accuracy of 95.54% for the Decision Tree of the OT-MQTT dataset and 96.19% for the random forest. |
| first_indexed | 2024-04-24T10:55:18Z |
| format | Article |
| id | doaj.art-5a547f332fff4725aa2a2f5e407789c1 |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-04-24T10:55:18Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-5a547f332fff4725aa2a2f5e407789c12024-04-12T07:36:29ZengEDP SciencesBIO Web of Conferences2117-44582024-01-01970016410.1051/bioconf/20249700164bioconf_iscku2024_00164Improving the Security of Internet of Things (IoT) Applications Based on a New Machine Learning TechniqueHussein Alia A.0Ramadhan Ali J.1TaeiZadeh Ali2Hussein Issa Mohand3University of AlkafeelUniversity of AlkafeelUniversity of QomMinistry of Education, Directorate of EducationThe Internet of Things, or IoT, is changing practically every aspect of modern life and entering both the business and residential domains. The proliferation of IoT has led to a rise in the frequency of cyber catastrophes. Attackers are using new methods or changing old ones, making the danger more sophisticated. The threat landscape that security professionals face is dynamic, complex, and diversified. This paper proposes a novel approach to enhance Internet of Things applications by fusing the swarm intelligence of Salp Swarm Algorithms (SSA) with the predictive power of Random Forest (RF) and Decision Tree (DT) models. Salp Swarm Algorithms simulate the cooperative behavior of salps in the natural world, wherein individual agents coordinate their actions to achieve common goals. This work uses SSA to optimize the Random Forest and Decision Tree model training process in an IoT context. SSA's collaborative nature makes it easier to explore the solution space effectively, which enhances the models' ability to capture the complex correlations found in IoT data. The effectiveness of the model is evaluated. We were able to attain a maximum accuracy of 95.54% for the Decision Tree of the OT-MQTT dataset and 96.19% for the random forest.https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00164.pdf |
| spellingShingle | Hussein Alia A. Ramadhan Ali J. TaeiZadeh Ali Hussein Issa Mohand Improving the Security of Internet of Things (IoT) Applications Based on a New Machine Learning Technique BIO Web of Conferences |
| title | Improving the Security of Internet of Things (IoT) Applications Based on a New Machine Learning Technique |
| title_full | Improving the Security of Internet of Things (IoT) Applications Based on a New Machine Learning Technique |
| title_fullStr | Improving the Security of Internet of Things (IoT) Applications Based on a New Machine Learning Technique |
| title_full_unstemmed | Improving the Security of Internet of Things (IoT) Applications Based on a New Machine Learning Technique |
| title_short | Improving the Security of Internet of Things (IoT) Applications Based on a New Machine Learning Technique |
| title_sort | improving the security of internet of things iot applications based on a new machine learning technique |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00164.pdf |
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