Reference : SOFIA: An Automated Security Oracle for Black-Box Testing of SQL-Injection Vulnerabilities |
Scientific congresses, symposiums and conference proceedings : Paper published in a book | |||
Engineering, computing & technology : Computer science | |||
Security, Reliability and Trust | |||
http://hdl.handle.net/10993/28150 | |||
SOFIA: An Automated Security Oracle for Black-Box Testing of SQL-Injection Vulnerabilities | |
English | |
Ceccato, Mariano ![]() | |
Nguyen, Duy Cu ![]() | |
Appelt, Dennis ![]() | |
Briand, Lionel ![]() | |
2016 | |
Proceedings of the 31th IEEE/ACM International Conference on Automated Software Engineering | |
Yes | |
No | |
International | |
31st IEEE/ACM International Conference on Automated Software Engineering (ASE 2016) | |
from 05-09-2016 to 07-09-2016 | |
[en] Web Application Security ; Penetration Testing ; Software Testing | |
[en] Security testing is a pivotal activity in engineering secure software. It consists of two phases: generating attack inputs to test the system, and assessing whether test executions expose any vulnerabilities. The latter phase is known as the security oracle problem.
In this work, we present SOFIA, a Security Oracle for SQL-Injection Vulnerabilities. SOFIA is programming-language and source-code independent, and can be used with various attack generation tools. Moreover, because it does not rely on known attacks for learning, SOFIA is meant to also detect types of \sqli attacks that might be unknown at learning time. The oracle challenge is recast as a one-class classification problem where we learn to characterise legitimate SQL statements to accurately distinguish them from \sqli attack statements. We have carried out an experimental validation on six applications, among which two are large and widely-used. SOFIA was used to detect real \sqli vulnerabilities with inputs generated by three attack generation tools. The obtained results show that SOFIA is computationally fast and achieves a recall rate of 100\% (i.e., missing no attacks) with a low false positive rate (0.6\%). | |
University of Luxembourg: High Performance Computing - ULHPC | |
Fonds National de la Recherche - FnR ; FBK Mobility project | |
Researchers ; Professionals ; Students | |
http://hdl.handle.net/10993/28150 | |
FnR ; FNR4800382 > Dennis Appelt > > Black-Box Security Testing for Web Applications and Services > 01/10/2012 > 30/06/2016 > 2012 |
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