Fusion-Based Hybrid Model for SMS Spam Detection Integrating Local, Sequential, and Contextual Features

In the era of pervasive digital communication, SMS spam poses significant threats, including financial fraud and phishing attacks, necessitating robust detection mechanisms. This paper introduces a novel hybrid model for SMS spam detection, leveraging advanced deep-learning techniques to capture diverse textual features comprehensively. The model integrates Convolutional Neural Networks (CNN) for local feature extraction, Bidirectional Long Short-Term Memory (Bi-LSTM) networks for sequential dependencies, and Bidirectional Encoder Representations from Transformers (BERT) for contextual embeddings. A parallel architecture combines these components to achieve a holistic representation of SMS content. Fused feature vectors undergo attention-based selection to enhance computational efficiency while preserving critical information. Evaluated on the UCI SMS Spam Collection dataset using a 10-fold cross-validation strategy, the proposed model achieves a remarkable accuracy of 99.68%, outperforming state-of-the-art techniques. This work addresses the limitations of traditional and hybrid methods, offering a highly reliable and adaptable solution to the evolving challenges of SMS spam detection. Future directions include real-time adaptability, multimodal integration, and resource-efficient deployment.