Abstract
Nowadays, the information technologies are profoundly affecting the way we produce and live. At the same time, various security threats in the cyberspace are constantly causing various security problems, such as SQL injection. Traditional SQL injection detection methods are often difficult to obtain good detection performance under actual circumstances. This paper models the SQL injection detection problem as a classification problem based on the mainstream LSTM model, CNN model and tries to combine and compare the training results to improve the detection accuracy. Also, the convolution neural network model based on attention is adopted for data representation. Experiments are carried out to demonstrate the performance of the proposed method.
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Shi, W., Liu, X. (2022). Research on SQL Injection Defense Technology Based on Deep Learning. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_45
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DOI: https://doi.org/10.1007/978-3-031-06788-4_45
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