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Traditional machine-learning techniques and subsequent deep-learning frameworks have shown limitations in handling the complex malicious URL data generated by contemporary phishing attacks. This paper proposes a novel detection framework, HSSLC-CharGRU (Hybrid Spatial\u2013Sequential Attention Logically constrained neural network CharGRU), which balances high efficiency and accuracy while enhancing the generalization capability of detection frameworks. The core of HSSLC-CharGRU is the Gated Recurrent Unit (Gated Recurrent Unit, GRU), integrated with the HSSA (Hybrid Spatial\u2013Sequential Attention, HSSA) module. The HSSLC-CharGRU framework proposed in this paper integrates symmetry concepts into its design. The HSSA module extracts URL sequence features across scales, reflecting multi-scale invariance. The interaction between the GRU and HSSA modules provides functional complementarity and symmetry, enhancing model robustness. In addition, the LCNN module incorporates logical rules and prior constraints to regulate the pattern-learning process during feature extraction, reducing the model\u2019s sensitivity to noise and anomalous patterns. This enhances the structural symmetry of the feature space. Such logical constraints further improve the model\u2019s generalization capability across diverse data distributions and strengthen its stability in handling complex URL patterns. These symmetries boost the model\u2019s generalization across datasets and its adaptability and robustness in complex URL patterns. In the experimental part, HSSLC-CharGRU shows excellent detection accuracy compared with the current character-level malicious URL detection models.<\/jats:p>","DOI":"10.3390\/sym17070987","type":"journal-article","created":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T10:44:41Z","timestamp":1750761881000},"page":"987","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Malicious URL Detection Framework Based on Custom Hybrid Spatial Sequence Attention and Logic Constraint Neural Network"],"prefix":"10.3390","volume":"17","author":[{"given":"Jinyang","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Hainan Normal University, Haikou 571158, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9195-8000","authenticated-orcid":false,"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Hainan Normal University, Haikou 571158, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Zheng","sequence":"additional","affiliation":[{"name":"Hainan Engineering Research Center for Virtual Reality Technology and Systems, Hainan Vocational University of Science and Technology, Haikou 571126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Hainan Normal University, Haikou 571158, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Hainan Normal University, Haikou 571158, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Foreign Languages, Hainan Normal University, Haikou 571158, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiling","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Hainan Normal University, Haikou 571158, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,23]]},"reference":[{"key":"ref_1","unstructured":"Proofpoint (2024). 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