A Review on Machine Learning Models for Mobile Text Spam Classification

Spam messages today are far more sophisticated than they used to be. Attackers frequently modify message structures, incorporate abbreviations, and use social engineering techniques to deceive users. Rule-based filters struggle to keep up with this evolving nature of spam—every new variation demands manual updates. Machine learning models outperform rule-based systems by learning contextual and statistical features from vast datasets, enabling them to detect even unseen patterns of spam. This adaptability makes these models essential for robust spam control. This paper presents a comprehensive survey on redirection spamming attack detection using artificial intelligence based approaches so as to thwart spamming attacks for time critical applications. Various models have been discussed with their pros and cons.