Forgery Domain Names Detection with Multi-Scale Convolution and Attention Mechanism
In order to tackle the problems that existing malicious domain name detection methods mainly use characters and word features to construct classifiers,which can easily lead to false negative of new generation or new varieties of forgery domain names.A forgery do-main names detection with multi-scale convolution and attention mechanism is proposed.Firstly,Transformer encoder improved by long short-term memory(LSTM)is used to capture multi-scale features of domain name string fine-grainedly.Then,attention mechanism is uti-lized to fuse the multi-scale features and extract the feature information of domain name strings in the space and time sequence.Finally,reinforcement learning algorithm is introduced to optimize the proposed model in the end-to-end manner.The result of experiments on open-source forgery domain datasets shows that the proposed method can achieve 98.03% Accuracy,97.91% Precision,2.01% FPR,1.55% FNR and 98.18% F1-score in the binary classification task of normal domain names and forgery domain names.It also has the same obser-vation that the proposed method has better performance in the multi-classification task of multi-family forgery domain names.
forgery domain name detection modelmulti-scale convolutionattention mechanismTransformer encoder
国家科技期刊平台
NSTL
万方数据
