| 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 |
| DOI |
10.1007/s10462-023-10689-9 |
| 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. |
| Author Keywords |
Internet of Things; Web service; Smart city; Blockchain defined network; Structural self-organized deep belief network; Starling Murmuration Optimizer (SMO) |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001172202300001 |
| WoS Category |
Computer Science, Artificial Intelligence |
| Research Area |
Computer Science |
| PDF |
https://link.springer.com/content/pdf/10.1007/s10462-023-10689-9.pdf
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