Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2024.059018
Journal Menu
Special Issues
Table of Content

All issues

Online First

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

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

Xin Tong1, Jingya Wang1,*, Ying Yang2, Tian Peng3, Hanming Zhai1, Guangming Ling4
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
* Corresponding Author: Jingya Wang. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.059018

Received 26 September 2024; Accepted 20 November 2024; Published online 20 December 2024

Abstract

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.

Keywords

Transformers; malicious SMS; multi-task learning; large language models

  • 36

    View

  • 8

    Download

  • 0

    Like

Related articles
Copyright© 2024 Tech Science Press
© 1997-2024 TSP (Henderson, USA) unless otherwise stated

Share Link