SQL Injection Attack Detection Technology Based on Timing Network
With the rapid development of modern information technology,injection vulnerabilities have been at the top of the top 10 of open Web application security projects for many years,and are one of the most damaging and widely exploited types of vul-nerabilities against Web applications.Structured query language(SQL)injection attack detection is still a challenging problem due to the heterogeneity of attack loads,the diversity of attack methods and the diversity of attack modes.At present,most of the main-stream SQL injection detection tools on the market are based on established rules and cannot meet the changing challenges.In this regard,this paper proposes a deep learning method,which uses context embedding model(BERT)to extract data set features,then uses BiLSTM's sequence modeling capability to further process sequence data,capture contextual dependencies and semantic rela-tionships,and finally uses attention mechanism as a classification algorithm.Experiments show that the proposed algorithm has a re-markable improvement in detection performance.
deep learningSQL injection attackBERTAttention mechanism
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