Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99523
Title: Mining input sanitization patterns for predicting SQL injection and cross site scripting vulnerabilities
Authors: Shar, Lwin Khin
Tan, Hee Beng Kuan
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Abstract: 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.
URI: https://hdl.handle.net/10356/99523
http://hdl.handle.net/10220/12857
DOI: 10.1109/ICSE.2012.6227096
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

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