Visible to the public A GAN-based Method for Generating SQL Injection Attack Samples

TitleA GAN-based Method for Generating SQL Injection Attack Samples
Publication TypeConference Paper
Year of Publication2022
AuthorsLu, Dongzhe, Fei, Jinlong, Liu, Long, Li, Zecun
Conference Name2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
Keywordsartificial intelligence, data augmentation, Data models, generative adversarial network, generative adversarial networks, genetic algorithm, Human Behavior, Market research, Metrics, policy-based governance, privacy, pubcrawl, resilience, Resiliency, security, SQL Injection, SQL injection detection, usability, Web vulnerability
AbstractDue to the simplicity of implementation and high threat level, SQL injection attacks are one of the oldest, most prevalent, and most destructive types of security attacks on Web-based information systems. With the continuous development and maturity of artificial intelligence technology, it has been a general trend to use AI technology to detect SQL injection. The selection of the sample set is the deciding factor of whether AI algorithms can achieve good results, but dataset with tagged specific category labels are difficult to obtain. This paper focuses on data augmentation to learn similar feature representations from the original data to improve the accuracy of classification models. In this paper, deep convolutional generative adversarial networks combined with genetic algorithms are applied to the field of Web vulnerability attacks, aiming to solve the problem of insufficient number of SQL injection samples. This method is also expected to be applied to sample generation for other types of vulnerability attacks.
NotesISSN: 2693-2865
DOI10.1109/ITAIC54216.2022.9836726
Citation Keylu_gan-based_2022