SQL injection attack detection method based on textCNN

Zhixin Xia; Jingwei Shao; Lu Yu; Jinggang Sun; Xiaochuan Yang; Heng Xu; Chao Liu; Jiadong Ren

doi:10.1117/12.3038647

22 July 2024 SQL injection attack detection method based on textCNN

Zhixin Xia, Jingwei Shao, Lu Yu, Jinggang Sun, Xiaochuan Yang, Heng Xu, Chao Liu, Jiadong Ren

Proceedings Volume 13222, International Conference on Signal Processing and Communication Security (ICSPCS 2024); 1322219 (2024) https://doi.org/10.1117/12.3038647
Event: Third International Conference on Signal Processing and Communication Security (ICSPCS 2024), 2024, Chengdu, China

Abstract

Structured Query Language Injection (SQLI) has always been a prominent threat in the field of cybersecurity, targeting web applications. Traditional techniques to prevent SQL injection typically rely on rule matching or dynamic and static analysis, which has high false positive rate and low detection efficiency. However, using deep learning algorithms can effectively detect SQL injection attacks and, consequently, overcome these issues. This paper proposes a SQL injection attack detection method based on TextCNN from the perspective of natural language processing and deep learning. Firstly, Word2Vec was used to process the text vectorization of SQL injection attack data, and then one-dimensional convolution was used to extract the features of the vectorized data. Finally, the trained SQL injection attack detection model was used to detect SQL injection attack. Experimental results show that the proposed method can effectively detect SQL injection attacks, and its accuracy rate, accuracy rate, recall rate, F1 value all achieved over 95%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhixin Xia, Jingwei Shao, Lu Yu, Jinggang Sun, Xiaochuan Yang, Heng Xu, Chao Liu, and Jiadong Ren "SQL injection attack detection method based on textCNN", Proc. SPIE 13222, International Conference on Signal Processing and Communication Security (ICSPCS 2024), 1322219 (22 July 2024); https://doi.org/10.1117/12.3038647

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KEYWORDS

Education and training

Data modeling

Convolution

Matrices

Statistical modeling

Neurons

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