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

TL;DR

This paper presents a dynamic deep ensemble model combining convolutional neural networks and traditional classifiers to improve spam detection in text, achieving high accuracy with reduced manual feature engineering.

Contribution

It introduces a novel adaptive deep ensemble approach that automatically extracts features and adjusts complexity, enhancing spam detection performance.

Findings

Achieved 98.38% accuracy in spam detection

Outperformed traditional machine learning and deep learning models

Effectively combined CNNs with ensemble classifiers like random forests

Abstract

The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people's fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38\%.