CLOUD COMPUTING-BASED SECURITY ANALYSIS ON WIRELESS SENSOR NODES CLUSTER USING PREDICTIVE TECHNIQUE

Rapid technological advancements have led to the widespread deployment of wireless sensor networks (WSNs) in industrial environments, making cybersecurity a critical concern in cloud computing. This paper presents a predictive framework for cloud-based intrusion detection and prevention for WSNs. It integrates machine learning models—Multilayer Perceptron (MLP), Decision Tree, and Autoencoder—to precisely classify and mitigate various impacts of cyber intrusions on a cluster of wireless sensors. An intelligent prioritization and prevention system is also proposed, categorizing attacks—blackhole, grayhole, flooding, and scheduling—based on their impact on industrial processes. Experimental results indicate robust detection capabilities, with the Decision Tree achieving 99.48% accuracy, slightlyoutperforming MLP at 99.37%. The Autoencoder demonstrated superior binary classification, distinguishing between normal and anomalous instances with high precision and recall rates. This framework leverages the WSN-DS dataset to simulate and validate its efficiency in mitigating real-time threats. Future work will focus on refining the prioritization model and integrating advanced machine learning techniques for enhanced adaptability and resilience.