计算机应用与软件2025，Vol.42Issue(5)：332-340,9.DOI:10.3969/j.issn.1000-386x.2025.05.044
CBi_AT:基于字符级和单词级的恶意URL检测
CBI_AT:MALICIOUS URL DETECTION BASED ON CHARACTERS AND WORDS LEVEL
摘要
Abstract
Aimed at the problem of efficient detection of malicious URLs,the current detection methods based on blacklist are poor in timeliness and adaptability,and the methods based on traditional machine learning are low in efficiency and accuracy.This paper fully considered the semantic meaning and temporal characteristics of URL,and proposed a hybrid neural network model(CBi_AT).URL was processed from the level of character and word at the same time,for capturing the semantic meaning and temporal features of URL strings effectively.Multi-group attention mechanism was introduced to extract the correlation and dependency between URL data.The experimental results show that the hybrid neural network model can detect malicious URL efficiently,with an accuracy of 99.86％and a F1 score of 99.85％.关键词
网络安全/恶意URL/混合神经网络模型/注意力机制Key words
Network security/Malicious URL/Hybrid neural network model/Attention mechanism分类
信息技术与安全科学引用本文复制引用
郭应政,袁建廷,钱育蓉..CBi_AT:基于字符级和单词级的恶意URL检测[J].计算机应用与软件,2025,42(5):332-340,9.基金项目
国家自然科学基金项目(61966035) （61966035）
国家自然科学基金联合基金项目(U1803261) （U1803261）
自治区科技厅国际合作项目(2020E01023) （2020E01023）
智能多模态信息处理团队项目(XJEDU2017T002). （XJEDU2017T002）
