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Traditional methods for detecting these attacks mainly rely on manually defined features, making these detection outcomes highly dependent on the precision of feature extraction. Unfortunately, these approaches struggle to adapt to the increasingly sophisticated nature of these attack techniques, thereby necessitating the development of more robust detection strategies. This paper presents a novel deep learning framework that integrates Bidirectional Encoder Representations from Transformers (BERT) and Long Short\u2010Term Memory (LSTM) networks, enhancing the detection of SQL injection attacks. Leveraging the advanced contextual encoding capabilities of BERT and the sequential data processing ability of LSTM networks, the proposed model dynamically extracts word and sentence\u2010level features, subsequently generating embedding vectors that effectively identify malicious SQL query patterns. Experimental results indicate that our method achieves accuracy, precision, recall, and F1 scores of 0.973, 0.963, 0.962, and 0.958, respectively, while ensuring high computational efficiency.<\/jats:p>","DOI":"10.1049\/2024\/5565950","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T22:35:09Z","timestamp":1712356509000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Deep Learning in Cybersecurity: A Hybrid BERT\u2013LSTM Network for SQL Injection Attack Detection"],"prefix":"10.1049","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1737-2009","authenticated-orcid":false,"given":"Yixian","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3607-3711","authenticated-orcid":false,"given":"Yupeng","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2024,4,5]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/technologies11040107"},{"key":"e_1_2_10_2_2","unstructured":"OWASP OWASP top ten 2021 https:\/\/owasp.org\/Top10\/."},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/jcp2040039"},{"key":"e_1_2_10_4_2","doi-asserted-by":"crossref","unstructured":"KatoleA. 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