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Wireless Sensors Network (WSN) face various challenges such as localization, security issues, deployment of node, clustering, and data aggregation. In addition, certain technological issues in WSN such as spatial distribution, higher energy utilization, small memory, bandwidth, and so on are also noticed. The proposed protocol comprises of four various stages such as (1) user registration, (2) remedy, (3) attack prediction, and (4) reloading. Initially, a new Modified Elliptic Curve Cryptography (ECC) model is introduced to ensure the secure communication. Moreover, the implemented approach also focuses on the attack prediction phase, where the Optimized Recurrent Neural Network (RNN) model is exploited for detecting the Denial-of-Service (DoS) attack. Here, the training of RNN is carried out by Shark Smell Optimization with Opposition Learning and Mutation Process (SSOL-MP) via tuning the optimal weights. At the end, analysis is done for validating the enhancement of the suggested model. [ABSTRACT FROM AUTHOR]