To address the issues of single feature extraction and low detection accuracy in current malicious URL detection models when handling URLs with complex structures and diverse character combinations,this paper proposes a malicious URL detection model based on multi-scale attention feature fusion.First,Character Embeddings and DistilBERT are employed to encode characters and words separately,capturing both character-level and word-level feature representations in URL strings.Next,an improved convolutional neural network(CNN)is used to extract multi-scale character structural features and word-level semantic features,while a bidirectional long short-term memory(BiLSTM)network is employed to further extract deep sequence features.Additionally,an innovative attention feature fusion(AFF)module is introduced to dynamically fuse multi-scale features at both the character and word levels,effectively reducing information redundancy and enhancing the extraction of long-range sequence features.Experimental results show that the proposed model outperforms other baseline models,with accuracy improvements ranging from 0.32%to 4.7%and F1 score improvements from 0.46%to 5.5%,achieving excellent detection performance on datasets such as ISCX-URL2016.
NETL
NSTL
万方数据
