期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：6
DOI：10.14569/IJACSA.2016.070616
出版社：Science and Information Society (SAI)
摘要：SQL Injection (SQLI) is one of the most popular vulnerabilities of web applications. The consequences of SQL injection attack include the possibility of stealing sensitive information or bypassing authentication procedures. SQL injection attacks have different forms and variations. One difficulty in detecting malicious attacks is that such attacks do not have a specific pattern. A new fuzzy rule-based classification system (FBRCS) can tackle the requirements of the current stage of security measures. This paper proposes a genetic fuzzy system for detection of SQLI where not only the accuracy is a priority, but also the learning and the flexibility of the obtained rules. To create the rules having high generalization capabilities, our algorithm builds on initial rules, data-dependent parameters, and an enhancing function that modifies the rule evaluation measures. The enhancing function helps to assess the candidate rules more effectively based on decision subspace. The proposed system has been evaluated using a number of well-known data sets. Results show a significant enhancement in the detection procedure
