An approach to minimize false positive in SQLI vulnerabilities detection techniques through data mining

Dependence on web applications is increasing very rapidly in recent time for social communications, health problem, financial transaction and many other purposes. Unfortunately, the presence of security weaknesses in web applications allows malicious user's to exploit various security vulnerabilities and become the reason of their failure. Currently, SQL Injection (SQLI) attacks exploit most dangerous security vulnerabilities in various popular web applications i.e. eBay, Google, Facebook, Twitter etc. Research on taint based vulnerability detection has been quite intensive in the past decade. However, these techniques are not free from false positive and false negative results. In this paper, we propose an approach to minimize false positive in SQLI vulnerability detection techniques using data mining concepts. We have implemented a prototype tool for PHP, MySQL technologies and evaluated it on six real world applications and NIST Benchmarks. Our evaluation and comparison results show that proposed technique detects SQLI vulnerabilities with low percentage of false positives.