Predicting SQL injection and cross site scripting vulnerabilities through mining input sanitization patterns

• Main Authors: SHAR, Lwin Khin, TAN, Hee Beng Kuan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: Vulnerability prediction; Data mining; Web application vulnerability; Input sanitization; Static code attributes; Empirical study; Data Storage Systems; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4896; https://ink.library.smu.edu.sg/context/sis_research/article/5899/viewcontent/Predicting___PV.pdf
• Institution: Singapore Management University

ContextSQL injection (SQLI) and cross site scripting (XSS) are the two most common and serious web application vulnerabilities for the past decade. To mitigate these two security threats, many vulnerability detection approaches based on static and dynamic taint analysis techniques have been proposed...