LEGF-DST:LLMs-Enhanced Graph-Fusion Dual-Stream Transformer for Fine-Grained Chinese Malicious SMS Detection  

作　　者：Xin Tong Jingya Wang Ying Yang Tian Peng Hanming Zhai Guangming Ling 

机构地区：[1]School of Information and Cybersecurity,People’s Public Security University of China,Beijing,100038,China [2]Cyber Investigation Technology Research and Development Center,The Third Research Institute of the Ministry of Public Security,Shanghai,201204,China [3]Department of Cybersecurity Defense,Beijing Police College,Beijing,102202,China [4]School of Computer Science,Henan Institute of Engineering,Zhengzhou,451191,China

出　　处：《Computers, Materials & Continua》2025年第2期1901-1924,共24页计算机、材料和连续体(英文)

基　　金：supported by the Fundamental Research Funds for the Central Universities(2024JKF13);the Beijing Municipal Education Commission General Program of Science and Technology(No.KM202414019003).

摘　　要：With the widespread use of SMS(Short Message Service),the proliferation of malicious SMS has emerged as a pressing societal issue.While deep learning-based text classifiers offer promise,they often exhibit suboptimal performance in fine-grained detection tasks,primarily due to imbalanced datasets and insufficient model representation capabilities.To address this challenge,this paper proposes an LLMs-enhanced graph fusion dual-stream Transformer model for fine-grained Chinese malicious SMS detection.During the data processing stage,Large Language Models(LLMs)are employed for data augmentation,mitigating dataset imbalance.In the data input stage,both word-level and character-level features are utilized as model inputs,enhancing the richness of features and preventing information loss.A dual-stream Transformer serves as the backbone network in the learning representation stage,complemented by a graph-based feature fusion mechanism.At the output stage,both supervised classification cross-entropy loss and supervised contrastive learning loss are used as multi-task optimization objectives,further enhancing the model’s feature representation.Experimental results demonstrate that the proposed method significantly outperforms baselines on a publicly available Chinese malicious SMS dataset.

关 键 词：Transformers malicious SMS multi-task learning large language models 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]