Feature extraction of web application attacks based on zeta distributions

With the advances in information technology, a computer has been embedded to many device used in everyday life. On the other hand, the report of damage concerning web application attacks is increasing these days, so these devices are facing a growing threat from web application attacks. Since it is not easy to cope with the automatic detection of the diversifying web application attacks using the black-list matching method, a lot of studies using machine learning method have been done in recently. In this paper, we will investigate the distribution of symbols in SQL injection attacks, and showed that the distribution can be approximated by a zeta distribution.