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The increase in people's use of mobile messaging services has led to thespread of social engineering attacks like phishing, considering that spam textis one of the main factors in the dissemination of phishing attacks to stealsensitive data such as credit cards and passwords. In addition, rumors andincorrect medical information regarding the COVID-19 pandemic are widely sharedon social media leading to people's fear and confusion. Thus, filtering spamcontent is vital to reduce risks and threats. Previous studies relied onmachine learning and deep learning approaches for spam classification, butthese approaches have two limitations. Machine learning models require manualfeature engineering, whereas deep neural networks require a high computationalcost. This paper introduces a dynamic deep ensemble model for spam detectionthat adjusts its complexity and extracts features automatically. The proposedmodel utilizes convolutional and pooling layers for feature extraction alongwith base classifiers such as random forests and extremely randomized trees forclassifying texts into spam or legitimate ones. Moreover, the model employsensemble learning procedures like boosting and bagging. As a result, the modelachieved high precision, recall, f1-score and accuracy of 98.38\%.

Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text