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DOI: 10.14569/IJACSA.2016.070616
PDF

Detection of SQL Injection Using a Genetic Fuzzy Classifier System

Author 1: Christine Basta
Author 2: Ahmed elfatatry
Author 3: Saad Darwish

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 6, 2016.

  • Abstract and Keywords
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Abstract: 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

Keywords: SQL injection; web security; genetic fuzzy system; fuzzy rule learning

Christine Basta, Ahmed elfatatry and Saad Darwish, “Detection of SQL Injection Using a Genetic Fuzzy Classifier System” International Journal of Advanced Computer Science and Applications(IJACSA), 7(6), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070616

@article{Basta2016,
title = {Detection of SQL Injection Using a Genetic Fuzzy Classifier System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070616},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070616},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {6},
author = {Christine Basta and Ahmed elfatatry and Saad Darwish}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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