Research on malicious URL detection based on multi-scale attention feature fusion
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.
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