PhishNet: A Real-Time, Scalable Ensemble Framework for Smishing Attack Detection Using Transformers and LLMs

The surge in smishing attacks underscores the urgent need for robust, real-time detection systems powered by advanced deep learning models. This paper introduces PhishNet, a novel ensemble learning framework that integrates transformer-based models (RoBERTa) and large language models (LLMs) (GPT-OSS 120B, LLaMA3.3 70B, and Qwen3 32B) to enhance smishing detection performance significantly. To mitigate class imbalance, we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques. Our system employs a dual-layer voting mechanism: weighted majority voting among LLMs and a final ensemble vote to classify messages as ham, spam, or smishing. Experimental results show an average accuracy improvement from 96% to 98.5% compared to the best standalone transformer, and from 93% to 98.5% when compared to LLMs across datasets. Furthermore, we present a real-time, user-friendly application to operationalize our detection model for practical use. PhishNet demonstrates superior scalability, usability, and detection accuracy, filling critical gaps in current smishing detection methodologies.