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IEEE/ICACT20250142 Slide.05        [Big Slide]	Oral Presentation
	Our proposed smishing detection method (Figure) first gathered labeled valid and spam texts from a publicly available Kaggle SMS dataset. We cleaned, standardized the data by removing special characters, digits, and punctuation, translating text to lowercase, eliminating stopwords, and applying lemmatization in the step of data preprocessing. Using a lightweight transformer model, TinyBERT, we extracted contextual embeddings of SMS messages. T-SNE visualization of these embeddings confirmed good feature separation. By balancing exploration and exploitation in the hyperparameter search space, the Aquila Optimization (AO) method helped us to finally maximize the hyperparameters of our deep learning model, hence producing excellent smishing detection results.

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