中圖分類號(hào)：TP393 4文獻(xiàn)標(biāo)識(shí)碼：ADOI:10.19358/j.issn.2097-1788.2024.01.003
引用格式：郝旭光.基于注意力特征融合網(wǎng)絡(luò)的DGA惡意域名檢測方法［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理，2024，43（1）：19-27.

A DGA malicious domain detection method based on attention feature fusion network

Abstract： Botnets employ Domain Generation Algorithms(DGA) to generate numerous random domain names to evade detection by the security defense system. In order to solve the problems of low accuracy and limited generalization capabilities, this article proposes attentional feature fusion network. This model combines an input layer, an Embedding layer, a Convolutional Neural Network layer, an attention module, and a Long Short Term Memory layer, achieving hierarchical feature extraction and substantially improving model′s performance. Experimental results indicate that the approach exhibits significant improvements in various indicators, showcasing outstanding DGA malicious domain name detection capabilities.

Key words : DGA domain;attention mechanism;neural network