网络与信息安全学报2025，Vol.11Issue(6)：77-91,15.DOI:10.11959/j.issn.2096-109x.2025066
APEA:一种恶意URL新型识别方法
APEA:a novel method for identifying malicious URL
摘要
Abstract
In the digital era,with the continuous evolution of cyberattack methods,the identification of malicious web pages has become a major challenge in the field of network security.Traditional convolutionalneural networks have achieved remarkable results in the detection of malicious web pages,but they are unable to capture long-distance dependencies and have limited semantic capture capabilities.Therefore,a novel deep learning modelnamed APEA was designed and implemented,offering a new research direction for the identification of malicious web-pages.APEA model adopted character-level information with a finer granularity than the Transformer to enhance the model's perception of detailed features in malicious URL texts.Similar to the Transformer,input embedding were combined with positional encoding to explicitly incorporate sequential information.Additionally,a global infor-mation sharing mechanism wasincorporated to effectively integrate local and global features,thereby improving the model's ability to recognize complex malicious URL patterns.An adaptive dynamic weight update mechanism was designed,enabling the model to flexibly adjust the weights of each head in the multi-head self-attention mechanism according to different input,thus capturing more features.Experimental results showed that compared with existing machine learning and deep learning-based malicious URL detection methods,APEA achieved better performance in terms of detection accuracy,precision,recall,and F1 score.Ablation experiment results indicated that both the global information sharing mechanism and the adaptive dynamic weight update mechanism contribute to the improvement of model performance.Compared with the model with these two mechanisms removed,the APEA model achieves an improvement of approximately 2.8%across all metrics..关键词
APEA/恶意URL识别/全局信息/TransformerKey words
APEA/malicious URL identification/global information/Transformer分类
信息技术与安全科学引用本文复制引用
ZHANG Huifei,YANG Xiuzhang,PENG Guojun..APEA:一种恶意URL新型识别方法[J].网络与信息安全学报,2025,11(6):77-91,15.基金项目
国家自然科学基金(62172308,62562012) （62172308,62562012）
贵州省科技计划项目(黔科合基础-zk[2025]面上686) （黔科合基础-zk[2025]面上686）
贵州省科技重大专项计划(黔科合重大专项字[2024]014) The National Natural Science Foundation of China(62172308,62562012),Guizhou Provincial Basic Re-search Program(Natural Science)(zk[2025]686),Major Scientific and Technological Special Project of Guizhou Province([2024]014) （黔科合重大专项字[2024]014）
