| Issue |
MATEC Web Conf.
Volume 173, 2018
2018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 5 | |
| Section | Modeling, Analysis, and Simulation of Intelligent Manufacturing Processes | |
| DOI | https://doi.org/10.1051/matecconf/201817301004 | |
| Published online | 19 June 2018 | |
Research on SQL injection detection technology based on SVM
1
Software Engineering School, Chongqing University of Posts and Telecommunication, Chongqing, China
2
Software Engineering School, Chongqing University of Posts and Telecommunication, Chongqing, China
3
Software Engineering School, Chongqing University of Posts and Telecommunication, Chongqing, China
SQL injection, which has the characteristics of great harm and fast variation, has always ranked the top of the OWASP TOP 10, which has always been a hot spot in the research of web security. In view of the difficulty of detecting unknown attacks by the existing rule matching method, a method of SQL injection detection based on machine learning is proposed. And the author analyses the method of SQL injection feature extraction, f Finally, the word2vec method is selected to process the text data of the HTTP request, which can effectively represent the SQL injection features containing the attack payload. Training and classification of processed samples with SVM algorithm, The experiment shows that this method effectively solves the problem of SQL injection to the mutation and the high leakage rate of the rule matching. By comparing with the classification results of statistical features, this SQL injection classification model has a higher detection rate.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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