Web Attack Detection Method Based on Support Vector Machines

TL;DR: The experimental results show that features after selection and extraction can reflect the nature of the original data and this method has higher detection rate.

Abstract: Web attack detection is a kind of dynamic Web security protection technology,but the intruder can use different coding schemes,mixed case,alternative statements and other skills,bypassing defense mechanism.For the particularity of web security and the shortage of the existing detection technology,we took SQL injection and cross site scripting attacks as an example.Firstly,the thesis studies the feature selection and extraction of SQL injection and cross site scripting attacks,and uses the artificial selection and mathematical statistical methods to covert the original payload into fixed dimension feature vector.Secondly,it marks the sample data after feature selection and extraction,and performs support vector machine training and classification.Finally,using the Weka,it verifies the feasibility and effectiveness of the approach.The experimental results show that features after selection and extraction can reflect the nature of the original data and this method has higher detection rate.