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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.<\/jats:p>","DOI":"10.1007\/s10462-023-10689-9","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T06:02:27Z","timestamp":1708668147000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An efficient starling murmuration-based secure web service model for smart city application using DBN"],"prefix":"10.1007","volume":"57","author":[{"given":"Adlin","family":"Sheeba","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. 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