Multimodal Fraudulent Website Identification Method Based on Heterogeneous Model Ensemble  

作　　者：Shengli Zhou Linqi Ruan Qingyang Xu Mincheng Chen 

机构地区：[1]Information Department of Zhejiang Police College,Hangzhou 310053,China [2]Zhejiang Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China(CNCERT/ZJ),Hangzhou 310052,China

出　　处：《China Communications》2023年第5期263-274,共12页中国通信（英文版）

基　　金：supported by Zhejiang Provincial Natural Science Foundation of China(Grant No.LGF20G030001);Ministry of Public Security Science and Technology Plan Project(2022LL16);Key scientific research projects of agricultural and social development in Hangzhou in 2020(202004A06).

摘　　要：The feature analysis of fraudulent websites is of great significance to the combat,prevention and control of telecom fraud crimes.Aiming to address the shortcomings of existing analytical approaches,i.e.single dimension and venerability to anti-reconnaissance,this paper adopts the Stacking,the ensemble learning algorithm,combines multiple modalities such as text,image and URL,and proposes a multimodal fraudulent website identification method by ensembling heterogeneous models.Crossvalidation is first used in the training of multiple largely different base classifiers that are strong in learning,such as BERT model,residual neural network(ResNet)and logistic regression model.Classification of the text,image and URL features are then performed respectively.The results of the base classifiers are taken as the input of the meta-classifier,and the output of which is eventually used as the final identification.The study indicates that the fusion method is more effective in identifying fraudulent websites than the single-modal method,and the recall is increased by at least 1%.In addition,the deployment of the algorithm to the real Internet environment shows the improvement of the identification accuracy by at least 1.9%compared with other fusion methods.

关 键 词：telecom fraud crime fraudulent website data fusion deep learning 

分 类 号：D924.35[政治法律—刑法学] TP393.08[政治法律—法学]