SQL INJECTION DETECTION USING PATTERN MATCHING ALGORITHM FOR LIBRARY SYSTEM

Abstract

Security concerned vulnerabilities are frequently detected and exploited in modern library system. Intruders obtain unrestricted access to the information stored in the library system by exploiting security vulnerabilities. It becomes a greater challenge for a library due to network acceptance and security vulnerability. Traditional library system is unable to detect malicious users from SQL injection attacks. Pattern matching algorithm has grown in prominence alongside the emergence of security awareness. In this work, an effective library system is proposed to detect SQL injection attacks by using static pattern matching algorithm. The proposed system makes use of an effective pattern matching algorithm and validation with the static pattern lists whether the authenticated user or not for the library system. It can update a new anomaly pattern to the existing static pattern list whether any form of new anomaly occurs. Moreover, the matching percentage of the attacks can be calculated after detection. The matching algorithm is modified to check how many percentages based on the defined threshold and it is applied to evaluate the accuracy of the system when SQL Infections are attacked. The evaluation is performed using the Bayes Classifier. The proposed system provides the output result with the possible percentage of SQL injection attacks entering the library system.