In this paper, we propose a hybrid rule-based approach, named as Optimal Email Feature Selection (OEFS) Protocol for selecting optimal features to reduce the searching time in detecting spam emails. The OEFS protocol performs email spam detection in four stages: Feature Selection, Normalization of selected features, Rank Assignment and Optimal Feature Selection. The OEFS protocol has been executed and designed for large data amount of data by achieving accurate feature generation. The performance of OEFS analyzed using different protocols in existing systems. The protocol defines here an optimality for email spam detection and correction which provides an optimal solution and outperforms all email filtering protocols like PEP and CRVSM.

Optimal Feature, Normalization, Score Assignment, Spam Email

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