IP-Augmented Multi-Modal Malicious URL Detection Via Token-Contrastive Representation Enhancement and Multi-Granularity Fusion
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| Format: | Preprint |
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2025
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| _version_ | 1866912646084689920 |
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| author | Tian, Ye Yu, Yanqiu Song, Liangliang Liu, Zhiquan Wang, Yanbin Sun, Jianguo |
| author_facet | Tian, Ye Yu, Yanqiu Song, Liangliang Liu, Zhiquan Wang, Yanbin Sun, Jianguo |
| contents | Malicious URL detection remains a critical cybersecurity challenge as adversaries increasingly employ sophisticated evasion techniques including obfuscation, character-level perturbations, and adversarial attacks. Although pre-trained language models (PLMs) like BERT have shown potential for URL analysis tasks, three limitations persist in current implementations: (1) inability to effectively model the non-natural hierarchical structure of URLs, (2) insufficient sensitivity to character-level obfuscation, and (3) lack of mechanisms to incorporate auxiliary network-level signals such as IP addresses-all essential for robust detection. To address these challenges, we propose CURL-IP, an advanced multi-modal detection framework incorporating three key innovations: (1) Token-Contrastive Representation Enhancer, which enhances subword token representations through token-aware contrastive learning to produce more discriminative and isotropic embeddings; (2) Cross-Layer Multi-Scale Aggregator, employing hierarchical aggregation of Transformer outputs via convolutional operations and gated MLPs to capture both local and global semantic patterns across layers; and (3) Blockwise Multi-Modal Coupler that decomposes URL-IP features into localized block units and computes cross-modal attention weights at the block level, enabling fine-grained inter-modal interaction. This architecture enables simultaneous preservation of fine-grained lexical cues, contextual semantics, and integration of network-level signals. Our evaluation on large-scale real-world datasets shows the framework significantly outperforms state-of-the-art baselines across binary and multi-class classification tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12395 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IP-Augmented Multi-Modal Malicious URL Detection Via Token-Contrastive Representation Enhancement and Multi-Granularity Fusion Tian, Ye Yu, Yanqiu Song, Liangliang Liu, Zhiquan Wang, Yanbin Sun, Jianguo Cryptography and Security Malicious URL detection remains a critical cybersecurity challenge as adversaries increasingly employ sophisticated evasion techniques including obfuscation, character-level perturbations, and adversarial attacks. Although pre-trained language models (PLMs) like BERT have shown potential for URL analysis tasks, three limitations persist in current implementations: (1) inability to effectively model the non-natural hierarchical structure of URLs, (2) insufficient sensitivity to character-level obfuscation, and (3) lack of mechanisms to incorporate auxiliary network-level signals such as IP addresses-all essential for robust detection. To address these challenges, we propose CURL-IP, an advanced multi-modal detection framework incorporating three key innovations: (1) Token-Contrastive Representation Enhancer, which enhances subword token representations through token-aware contrastive learning to produce more discriminative and isotropic embeddings; (2) Cross-Layer Multi-Scale Aggregator, employing hierarchical aggregation of Transformer outputs via convolutional operations and gated MLPs to capture both local and global semantic patterns across layers; and (3) Blockwise Multi-Modal Coupler that decomposes URL-IP features into localized block units and computes cross-modal attention weights at the block level, enabling fine-grained inter-modal interaction. This architecture enables simultaneous preservation of fine-grained lexical cues, contextual semantics, and integration of network-level signals. Our evaluation on large-scale real-world datasets shows the framework significantly outperforms state-of-the-art baselines across binary and multi-class classification tasks. |
| title | IP-Augmented Multi-Modal Malicious URL Detection Via Token-Contrastive Representation Enhancement and Multi-Granularity Fusion |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2510.12395 |