The integration of Wireless Sensor Networks (WSN) with cloud computing technologies represents a significant area of research focused on enhancing service delivery. Cloud computing employs many independent, intelligent devices capable of collecting and transmitting substantial volumes of data. This formation of heterogeneous networks introduces various research challenges, particularly concerning the processing, storing, and managing of extensive datasets. Moreover, cloud-based devices are subject to limited restrictions and may be susceptible to network attacks. To mitigate these concerns, this study proposes a Secured Cloud Architecture (SCA) to bolster WSN scalability while ensuring efficient data processing and robust security measures.This proposed framework uses cloud computing to securely store, process, and manage substantial quantities of data. This research emphasizes a practical methodology for categorizing network attacks through the innovative application of a combined Artificial Neural Network (ANN) and the Blowfish algorithm. Initially, the ANN-based classifier is deployed to differentiate between significant and non-significant data, followed by the optimal encryption of the important data using the Blowfish algorithm. The inherent properties of the Blowfish algor...