Cloud Computing Technology for Effectual Security Attack Detection in WSM using ANN with Blow Fish Algorithm

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, cloudbased 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 algorithm facilitate efficient data decryption, characterized by a high level of effectiveness and a rapid convergence rate.Encrypted data and non-significant information are stored within the cloud, where a decryption process enables the recovery of the original data format.The results of the experiments indicate that the proposed algorithm exhibits superior performance relative to existing methodologies, demonstrating enhanced accuracy, precision, recall, and F-measure.As such, it serves as an effective tool for detecting network attacks.