| Title | An efficient starling murmuration-based secure web service model for smart city application using DBN |
|---|---|
| ID_Doc | 37618 |
| Authors | Sheeba, A; Bushra, SN; Rajarajeswari, S; Subasini, CA |
| Title | An efficient starling murmuration-based secure web service model for smart city application using DBN |
| Year | 2024 |
| Published | Artificial Intelligence Review, 57.0, 3 |
| Abstract | The advent of IoT devices increased internet usage more than smartphones and personal computers. The manual analysis of the Web Service Description Language (WSDL) document is quite expensive and time-consuming, hence this paper proposes a novel deep learning architecture to overcome the issue associated with web service classification. A structural self-organized deep belief network (SSODBN) is used for real-time web service classification in different fields such as Education, Smart electricity, Intelligent road networks, Health and social care, and Sports, water, and gas distribution. The SSODBN architecture utilizes a dropout strategy to minimize the interrelationship between the feature detectors and a regularized reinforced transfer function to eliminate the irrelevant weights. The main advantage offered by the S-DBN architecture is the improved preprocessing with feature selection. The Starling Murmuration Optimizer (SMO) is utilized in this paper to minimize the reconstruction error of the S-DBN architecture. The security of the smart city architecture is mainly improved via the blockchain defined network (BDN) using user-authenticated blocks. The experimental results revealed that the proposed method managed the scalability, latency, and centralization issues with superior data transmission. |
| https://link.springer.com/content/pdf/10.1007/s10462-023-10689-9.pdf |
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