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

• Main Authors: SHAR, Lwin Khin, TAN, Hee Beng Kuan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: defect prediction; data mining; static code attributes; web security vulnerabilities; input sanitization; Information Security; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4679; https://ink.library.smu.edu.sg/context/sis_research/article/5682/viewcontent/Mining_input_sanitization_patterns_for_predicting_SQL_injection_and_cross_site_scripting_vulnerabilities_icse12.pdf
• Institution: Singapore Management University

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