Hussein Alaa Al-Kabbi1,2, Mohammad-Reza Feizi-Derakhshi1,*, Saeed Pashazadeh3
Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 665-682, 2024, DOI:10.32604/iasc.2024.050452
- 06 September 2024
Abstract SMS spam poses a significant challenge to maintaining user privacy and security. Recently, spammers have employed fraudulent writing styles to bypass spam detection systems. This paper introduces a novel two-level detection system that utilizes deep learning techniques for effective spam identification to address the challenge of sophisticated SMS spam. The system comprises five steps, beginning with the preprocessing of SMS data. RoBERTa word embedding is then applied to convert text into a numerical format for deep learning analysis. Feature extraction is performed using a Convolutional Neural Network (CNN) for word-level analysis and a Bidirectional Long… More >
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