Detecting the Abnormal SQL Query Using Hybrid SVM Classification Technique in Web Application

Abstract

Detecting SQL injection attacks (SQLIAs) is ending up progressively significant in database-driven sites. A large portion of the investigations on SQLIA detection have concentrated on the structured query language (SQL) structure at the application level. Yet, those methodologies unavoidably neglects to identify those attacks that utilization previously put away methodology and information inside the database framework. While most existing techniques tended to towards diminishing the quantity of support vectors, the proposed philosophy concentrated on decreasing the quantity of test datapoints that need SVMs assistance in getting grouped. The focal thought is to inexact the choice limit of SVM utilizing paired trees. The subsequent tree is a half and half tree as in it has both univariate and multivariate (SVM) nodes. The cross breed tree takes SVMs assistance just in ordering significant information focuses lying close choice limit staying less urgent datapoints are grouped by quick univariate nodes.