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| Title | Deep Trust: A Novel Framework For Dynamic Trust And Reputation Management In The Internet Of Things (Iot)-Based Networks |
|---|---|
| ID_Doc | 18089 |
| Authors | Ullah F.; Salam A.; Amin F.; Khan I.A.; Ahmed J.; Zaib S.A.; Choi G.S. |
| Year | 2024 |
| Published | IEEE Access, 12 |
| DOI | http://dx.doi.org/10.1109/ACCESS.2024.3409273 |
| Abstract | The Internet of Things (IoT) proliferation has brought unprecedented connectivity, introducing complex trust and reputation management challenges across vast, heterogeneous networks. This paper introduces the DeepTrust framework, a novel approach leveraging deep learning algorithms to dynamically assess and manage trust and reputation in IoT environments. We demonstrate the framework's superiority in accurately identifying trustworthy and untrustworthy devices through extensive experiments, significantly enhancing IoT security. DeepTrust has demonstrated marked superiority over existing methods, showcasing enhanced accuracy in identifying trustworthy versus untrustworthy devices, thereby significantly bolstering IoT network security. Specifically, our results reveal an improvement in accuracy by 15%, precision by 20%, and recall rates by 18% compared to conventional models, highlighting DeepTrust's effectiveness in real-time, adaptive trust assessments. There are several avenues for enhancing and expanding the DeepTrust framework. Future research will explore optimization techniques for reducing computational demands, enabling deployment on resource-constrained IoT devices. Additionally, incorporating incremental learning mechanisms could improve the framework's adaptability to new and changing IoT environments. Enhancing data privacy and security measures within the framework constitutes another critical development area, ensuring the protection of sensitive information used in trust assessments. Lastly, extending the framework's applicability across various IoT domains and applications presents a promising direction, aiming to establish a universal trust management solution adaptable to the unique requirements of different IoT ecosystems. By outlining these potential future directions, we aim to highlight the current achievements of the DeepTrust framework and chart a course for its continued development and refinement. This comprehensive approach underscores our commitment to advancing the field of IoT trust and reputation management, paving the way for more secure and reliable IoT networks. © 2013 IEEE. |
| Author Keywords | Adaptive trust; connectivity; data security; deep learning; Internet of Things; reputation management; security; smart cities; trust |
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