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.
