A hybrid correlation-based deep learning model for email spam classification using fuzzy inference system

Spam emails are unwanted and unsolicited messages. The major problems in email spam detection methods are low detection rates and a high likelihood of false alarms. This study proposes a hybrid correlation-based deep learning model for email spam classification using a fuzzy inference system. Using a rule-based hybrid feature selection technique, we choose the most crucial features from a preprocessed spam-based dataset. The selected features are then loaded into a deep learning model for spam classification, and fuzzy logic is employed to categorize each spam class into its severity levels to decrease misclassification. Compared with other machine learning methods, the proposed method shows better F1-score results for both test set spambase and test set non-spambase of 96.5% and 96.4%, respectively. Similarly, the developed method shows better accuracy, error rate, and processing time of 94.0017%, 5.9983%, and 0.5 s, respectively. The proposed approach also shows a reduced misclassification based on the fuzzy inference system. •Develop a hybrid correlation-based deep learning model of email spam classification using a fuzzy inference system.•A rule-based hybrid feature selection technique was used to choose the most relevant features from the preprocessed dataset.•Use fuzzy logic to categorize a particular spam class into various severity levels to reduce misclassification.•Feed the chosen features into a deep learning model for spam classification.•Show the results of the developed method is significant at p = 0.015 for the email spam classification.