Mining input sanitization patterns for predicting SQL injection and cross site scripting vulnerabilities

• Published in: 2012 34th International Conference on Software Engineering (ICSE) pp. 1293 - 1296
• Main Authors: Lwin Khin Shar, Hee Beng Kuan Tan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2012
• Subjects: Complexity theory; Data mining; Data models; defect prediction; HTML; input sanitization; Predictive models; Security; Software; static code attributes; web security vulnerabilities
• ISBN: 9781467310666; 1467310662
• ISSN: 0270-5257; 1558-1225
• DOI: 10.1109/ICSE.2012.6227096

Static code attributes such as lines of code and cyclomatic complexity have been shown to be useful indicators of defects in software modules. As web applications adopt input sanitization routines to prevent web security risks, static code attributes that represent the characteristics of these routines may be useful for predicting web application vulnerabilities. In this paper, we classify various input sanitization methods into different types and propose a set of static code attributes that represent these types. Then we use data mining methods to predict SQL injection and cross site scripting vulnerabilities in web applications. Preliminary experiments show that our proposed attributes are important indicators of such vulnerabilities.