AI‐Driven SMS Intelligence Framework for Detecting Fraud, Disinformation, and Threat Patterns in Pakistan’s Communication Networks

Fraudulent SMS messages are a significant threat in Pakistan, impacting financial security, identity theft, and misinformation and enabling exploitation by adversaries. This study proposes a localized AI‐based framework for SMS fraud detection, utilizing machine learning (logistic regression [LR], random forest [RF], support vector machine [SVM], extreme gradient boosting [XGB]) and deep learning models (multilayer perceptron [MLP], long short‐term memory [LSTM]) alongside an ensemble voting classifier. The framework is built on a curated dataset of 5617 SMS messages, including fraud, promotional, and normal categories, which were preprocessed with tokenization, normalization, and TF–IDF feature extraction. Experimental results show that ensemble and tree‐based models achieved accuracy exceeding 0.95. Adversarial robustness testing, including synonym substitution, typo injection, noise addition, data poisoning, and label flipping, was conducted to evaluate model resilience. Although the models maintained high baseline accuracy, adversarial attacks underscore the need for robust evaluation in security‐critical applications. This research contributes to (i) the creation of a representative SMS dataset tailored to Pakistan, (ii) a comparative analysis of classical, deep, and ensemble learning models, and (iii) an extensive evaluation of adversarial robustness. Limitations include the monolingual dataset and restricted adversarial testing, which present opportunities for future research on multilingual datasets and real‐time deployment. To further strengthen the evaluation, this study incorporates transformer‐based multilingual models, such as mBERT and XLM‐R, that leverage contextual embeddings to improve semantic understanding. Experimental results show that these models outperform traditional and deep learning approaches, achieving up to 99.4% accuracy and a 99.2% F1 score. Additionally, an extended adversarial analysis demonstrates that all models experience performance degradation under attack; transformer‐based models maintain comparatively higher robustness to synonym substitution, MLP to noise injection and data poisoning, ensemble to typo injection, and finally RF to label flipping.